Computational pathology literature registry
This is the structured source behind the computational-pathology lists in My Library. It makes year, publication state, route membership, official artifacts, and the last metadata-check date visible in one place.
Coverage cutoff: 2 September 2026. “Current” means that the maintained routes and frontier searches were reviewed through that date. It does not mean that every paper ever written is included. Recent preprints are labeled separately from peer-reviewed work, and venue-accepted papers remain “accepted” until a proceedings record is available.
Showing 342 of 342 records.
- L3
A benchmark study of vision and pathology foundation models for computational pathology
Bareja et al.
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- L3
A Generative Foundation Model for Multimodal Histopathology
Jinxi Xiang, Mingjie Li, Siyu Hou, Yijiang Chen, Xiangde Luo, Yuanfeng Ji, Xiang Zhou, Ehsan Adeli, Akshay Chaudhari, Curtis P. Langlotz, Kilian M. Pohl, and Ruijiang Li
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- L3
A Magnification Alignment Framework Enables Computation- and Communication-Efficient Computational Pathology
Chu Han, Bingchao Zhao, Tianpeng Deng, Jingqi Huang, Fangfang Liu, Jiatai Lin, Shanshan Lyu, Longfei Wang, Cheng Lu, Changhong Liang, Hannah Y. Wen, Zhenwei Shi, Xiaojing Guo, and Zaiyi Liu
Peer-reviewed journal article 86(12):3060–3073.
Official artifacts: Author manuscript · Official code
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- L3
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- L3
Act Like a Pathologist: Tissue-Aware Whole Slide Image Reasoning
Wentao Huang et al.
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- L3
AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer
Zhe Li et al.
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- L3
ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
Jiawen Li, Tian Guan, Huijuan Shi, Xitong Ling, Mingxi Fu, Anjia Han, Chao He, and Yonghong He
Official artifacts: Code and models
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- L3
An end-to-end-trained vision-language model for native-language prostate pathology report generation
Christian Grashei, Fabian Gülhan, Maximilian Legnar, Fabian Stögbauer, Cleo-Aron Weis, Carolin Mogler, and Peter Schüffler
arXiv v1 preprint submitted 24 August 2026; no peer-reviewed record, public training code, model weights, or paired clinical dataset release was verified by the review cutoff.
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- L3
An evaluation of artificial intelligence assisted prostate biopsy reporting in the Articulate Pro study
Lisa Browning et al.
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- L3
Auditing Data Leakage in Whole-Slide Image Multimodal Benchmarks
Wenhao Zhang et al.
Submitted 2026-07-14; retain preprint status and evaluate its audit protocol independently.
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- L3
Benchmarking Pathology Foundation Models for Spatial Domain Understanding
Bokai Zhao, Yiyang Zhang, Yuanchi Zhu, Hanqing Chao, Long Bai, Tai Ma, Minfeng Xu, Ming Song, and Tianzi Jiang
MICCAI 2026 chapter, LNCS 16895, pages 35–45, first online 25 September 2026; the volume imprint says 2027. Review uses the inspected publisher PDF. The 42 slides comprise 12 expert-annotated DLPFC slides and 30 slides assessed using transcriptomic clustering proxies. The linked public repository is a prepared-results dashboard, not a verified end-to-end benchmark implementation.
Official artifacts: Publisher full chapter · Official MICCAI open-access record · Official accepted manuscript · Author results dashboard · Dashboard repository and documented limits
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- L3
Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation
Yumi Lee, Harim Oh, Hyoryung Kim, Minji Kim, Eunsu Kim, Hyeseong Lee, Junya Fukuoka, Andrey Bychkov, Jijgee Munkhdelger, Rajiv Kumar Kaushal, Ayushi Sahay, Rajni Yadav, Bharathi Prabakaran, Sulen Sarioglu, Serdar Balcı, Ilknur Turkmen, Yuri Tolkach, Christian Harder, Julian Westerdorf, Reinhard Buettner, Audun Ljone Henriksen, Sepp De Raedt, Byung Hyun Lee, Sungjin Lim, Joohoon Lee, Gwanghyun Kim, Se Young Chun, Suryakant Singh, Saarthak Kapse, Prateek Prasanna, Kyung A Kim, Yousun Kang, Sehwan Yoo, Sungman Hong, Shubham Innani, Michael Feldman, Spyridon Bakas, Ujjwal Baid, Prasad Dutande, Suhas Gajare, Bhakti Baheti, Serkan Sökmen, Ece Tuğba Cebeci, Ahmet Halıcı, Musa Balcı, Kardelen Peçenek, Srividhya Sainath, Kyongseok Jang, Messi H. J. Lee, Noorul Wahab, Bodong Du, Jiaming Zhang, Qixiang Zhang, Jang-Hwan Choi, and Sangjeong Ahn
arXiv v1 submitted 1 September 2026 reports the results of the MICCAI REG 2025 challenge. No final peer-reviewed proceedings or journal record was verified on 2 September 2026. The approximately 10,500 institutional WSI-report pairs are not a generally downloadable public dataset; the released repositories expose benchmark information and team artifacts.
Official artifacts: Official challenge repository · Official winning-system repository · Official challenge site
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- L3
Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Sweta Banerjee et al.
Official artifacts: Official code
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- L3
Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models
Clément Grisi, Jeroen van der Laak, and Geert Litjens
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- L3
Beyond Pixel Simulation: Pathology Image Generation via Diagnostic Semantic Tokens and Prototype Control
Minghao Han, Yichen Liu, Yizhou Liu, Zizhi Chen, Jingqun Tang, Xuecheng Wu, Dingkang Yang, and Lihua Zhang
Official artifacts: Author preprint · Official code and data
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- L3
Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning
Bryan Wong, Xun Xu, Huazhu Fu, Nancy F. Chen, and Mun Yong Yi
Official artifacts: Official code
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- L3
CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis
Di Zhang et al.
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- L3
CellDETR: A Detection-Guided Framework for Scalable Cell Representation Learning from Histopathology Images
Shikang Zhang, Guojun Li, Yicong Mao, and Chulin Sha
The official implementation describes a NeurIPS 2026 submission; no acceptance or proceedings record was available when checked on 2026-08-10.
Official artifacts: Official code
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- L3
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
Hörst et al.
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- L3
CMA-MIL: A Cross-Magnification Attention-Based Multiple Instance Learning Framework for Renal Cell Carcinoma Subtyping Using Whole-Slide Images
Rashika Bagri and Ankit Rajpal
Peer-reviewed journal article, volume 135, article 111204. The weakly supervised model uses 5×, 10×, and 20× streams and retains their cross-attention fusion at inference; it is not a low-magnification-only student. No official artifact was verified on 2026-09-02.
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- L3
Computationally Efficient Pathology Segmentation using Knowledge Distillation from Foundation Models
Jiaqi Lv, Yijie Zhu, Saki Okada, Maxwell Stanley Renna, Robert Goldin, Stefan Antonowicz, and Shan E Ahmed Raza
arXiv v1 (2026-09-03), submitted to Medical Image Analysis according to the authors, not accepted. Verified methods and selected results on 2026-10-02. TIAToolbox is the stated release destination; availability of these exact weights was not independently confirmed. Patch-throughput measurements are not end-to-end WSI latency.
Official artifacts: Versioned manuscript · TIAToolbox — stated release destination
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- L3
ConStruct: Structural Distillation of Foundation Models for Prototype-Based Weakly Supervised Histopathology Segmentation
Khang Le, Ha Thach, Anh M. Vu, Trang T. K. Vo, Han H. Huynh, David Yang, Minh H. N. Le, Thanh-Huy Nguyen, Akash Awasthi, Chandra Mohan, Zhu Han, and Hien Van Nguyen
Peer-reviewed MIDL 2026 proceedings, PMLR 315:2893–2905; official record verified 2026-10-02.
Official artifacts: Official proceedings PDF · Official implementation
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- L3
Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification
Zhang et al.
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- L3
CorePath: A Breast-Specialized Pathology Foundation Model for Core Needle Biopsy Diagnosis and Risk-Controlled Report Generation
Ting Yin et al.
The manuscript states that code will be released upon publication; no released implementation was claimed when checked on 2026-08-08.
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- L3
Cross-Stain Knowledge Distillation for Low-Cost Lung Cancer Programmed Death Ligand-1 Assessment With Multi-Granularity Multiple Instance Learning
Yi Shi, Chong Ge, Fang Zhao, Anli Zhang, Ao Li, Haibo Wu, and Minghui Wang
Peer-reviewed journal article, volume 167, article 113762. IHC is privileged teacher information during training; deployment uses the H&E student, but its macro- and micro-granularity branches remain. No official code or model release was verified on 2026-09-02.
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- L3
DaX: Learning General Pathology Representations Across Scales
Bokai Zhao, Yiyang Zhang, Long Bai, Tai Ma, Hanqing Chao, and Minfeng Xu
Official artifacts: Code
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- L3
Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction
Mayanka Chandrashekar, Xi Zhang, Ethan Seefried, Tirthankar Ghosal, John Gounley, and Heidi Hanson
arXiv v1 systems preprint submitted 27 August 2026; no peer-reviewed version, official implementation, or reusable deployment package was verified by the review cutoff.
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- L3
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- L2–L3
Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling
Sander Moonemans et al.
Official artifacts: Code, data, and model
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- L3
Deployment of AI-driven automated quality control of whole-slide images in a large tertiary cancer center
Kaitlyn Gelfant et al.
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- Research
DINOv3
Meta AI et al.
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- L3
Do Pathology Vision-Language Models Truly See Pathology?
Chengyang Zhang et al.
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- L3
Dual-Level Hypergraph Generation for Addressing Feature Scarcity in Whole-Slide Image Classification
Yao et al.
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- L3
EXPOSE: Explainable and Domain-Robust Embeddings from Pathology Vision Foundation Models using Sparse Autoencoders
Anja Witte, Maximilian Lennartz, Jan Baumbach, Guido Sauter, Stefan Bonn, Patrick Fuhlert, and Marina Zimmermann
Independently verified on the official eXCV 2026 Proceedings Track list, not ECCV main conference. Scientific review uses arXiv:2608.28191v1; a final typeset proceedings version was not verified. Evaluation is prostate tissue-microarray spots, not a whole-slide benchmark, and includes a thin-section failure case.
Official artifacts: Official workshop accepted-paper list · Author implementation · Versioned full manuscript and appendix
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- L3
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- L3
From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology
Basit Alawode, Moshira Ali Abdalla, Dwarikanath Mahapatra, Muhammad Muzammal Naseer, and Sajid Javed
ECCV 2026 acceptance is now verified by official poster 4422, superseding the preprint-only catalog status. A Springer version of record was not verified on 2026-09-12; the existing review remains tied to its declared manuscript revision.
Official artifacts: Official ECCV 2026 poster · Conference manuscript
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- L3
From Patches to Patients: A Study of the Tile-to-Slide Performance Transferability in Digital Pathology
Sofiène Boutaj, Leo Fillioux, Maria Vakalopoulou, Stergios Christodoulidis, and Pierre Marza
MICCAI 2026 chapter, LNCS 16891, pages 149–159; publisher first-online date is in September 2026, although the volume copyright/bibliographic imprint says 2027. Review uses the inspected publisher PDF, not only arXiv:2606.10778. Across-encoder rank correlation supports shortlisting, not guaranteed selection of the best model for a new clinical cohort.
Official artifacts: Publisher full chapter · Official MICCAI open-access record · Official accepted manuscript · Earlier arXiv manuscript
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- L3
GenBio-PathFM: A State-of-the-Art Foundation Model for Histopathology
Saarthak Kapse, Mehmet Aygün, Elijah Cole, Emma Lundberg, Le Song, and Eric P. Xing
Public technical report and weights; no peer-reviewed version was located by 2026-07-27.
Official artifacts: Code · Model
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- L3
Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images
Zhiwen Xu, Xiaoming Yan, Chengkun Wu, Juan Chen, Haoang Chi, and Liyang Xu
The arXiv record reports acceptance at ACM Multimedia 2026. The proceedings record was not yet available when checked on 2026-08-08.
Official artifacts: Official code
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- L3
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Naoto Usuyama et al.
Version 2 dated 2026-07-22; author-reported performance remains provisional.
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- L3
Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer
Zhiwei Chen, Yang Hu, Yuxiang Xiao, Yakun Ju, Tianyang Zhang, Yingxue Xu, Wei Li, Hao Chen, Jens Rittscher, and Kaixiang Yang
Official MICCAI 2026 proceedings record and accepted-version open-access PDF verified 2026-10-02; Springer DOI and pagination remain pending.
Official artifacts: Official code · Official MICCAI 2026 proceedings · Official accepted-version PDF
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- L2–L3
HistoMILKD: A Multiple Instance Learning based Multi-Teacher Knowledge Distillation Framework for Whole Slide Image Classification
Mayur Mallya, Ali Khajegili Mirabadi, Hossein Farahani, and Ali Bashashati
Official artifacts: Code
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- L3
Histopathology Image Report Generation by Vision Language Model with Multimodal In-Context Learning
Shih-Wen Liu et al.
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- L3
KDTViT Knowledge Distillation, Transfer Learning and Transformer Based Deep Learning Framework for Efficient Histopathology Image Classification
Aarv Mankodi, Praveen Kumar Shukla, and Hitesh Tekchandani
Peer-reviewed open-access journal article 16:20721. A MobileViT-XS is sequentially adapted from 40× through 400× using the previous checkpoint as teacher; BreakHis consists of biopsy images rather than WSIs, and the same-capacity teacher/student design targets retention rather than model compression.
Official artifacts: Open-access article
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- L2–L3
Knowledge-enhanced pretraining for vision-language pathology foundation model on cancer diagnosis
Xiao Zhou et al.
Official artifacts: Code · Model
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- L3
Label-free whole slide virtual multi-staining using dual-excitation photon absorption remote sensing microscopy
James E. D. Tweel, Benjamin R. Ecclestone, James A. Tummon Simmons, and Parsin Haji Reza
Author correction published 10 April 2026; the version-of-record page is the canonical updated article.
Official artifacts: Public held-out WSI pairs (BioImage Archive)
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- L3
LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models
Gangsu Kim and Won-Ki Jeong
Official MICCAI 2026 proceedings record and accepted-version open-access PDF verified 2026-10-02; this supersedes the prior preprint classification. Springer DOI remains pending.
Official artifacts: Official code · Official MICCAI 2026 proceedings · Official accepted-version PDF
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- L3
LanGuSTE: Language-Guided Coarse-to-Fine Patch Selection for Efficient Whole Slide Image Analysis
Yonghan Shin, Gangsu Kim, and Won-Ki Jeong
arXiv preprint first submitted in 2025 and substantially revised as v2 on 24 August 2026; no peer-reviewed version or official implementation was verified by the review cutoff.
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- L1–L2
LazySlide: accessible and interoperable whole-slide image analysis
Yimin Zheng, Ernesto Abila, Eva Chrenková, Iva Buljan, Juliane Winkler, and André F. Rendeiro
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- L3
Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data
Tianyu Liu et al.
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- L3
LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
Yonghan Shin and Won-Ki Jeong
Official MICCAI 2026 proceedings record and accepted-version open-access PDF verified 2026-10-02. Springer DOI and pagination remain pending; do not invent either.
Official artifacts: Official code · Official MICCAI 2026 proceedings · Official accepted-version PDF
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- L3
M2OTCA: Multiple-Magnification Optimal Transport-Based Cross-Attention Learning for Whole Slide Image Classification
Zhonghang Zhu, Erik Meijering, and Liansheng Wang
Peer-reviewed journal article, volume 112, article 104082. Region structural prototypes reduce the cost of optimal transport across 5×, 10×, and 20×; all three magnifications feed magnification-specific predictions and remain part of inference. No official artifact was verified on 2026-09-02.
Official artifacts: PubMed record
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- L3
Melan-Dx: a knowledge-enhanced vision-language framework improves differential diagnosis of melanocytic neoplasm pathology
Jialu Yao et al.
Official artifacts: Code
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- L3
Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
Daniel Shao, Joel Runevic, Richard J. Chen, Drew F. K. Williamson, Ahrong Kim, Andrew H. Song, and Faisal Mahmood
Official artifacts: Code
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- L3
MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding
Basit Alawode, Arif Mahmood, Muaz Khalifa Al Radi, Shahad Albastaki, Asim Khan, Muhammad Bilal, Moshira Ali Abdalla, Mohammed Bennamoun, and Sajid Javed
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- L3
Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fields
Lei Wu, Jiashuai Liu, Di Zhang, Zhangpeng Gong, Yingkang Zhan, Yi Niu, Jiusong Ge, Chunze Yang, Kai Yi, Mireia Crispin-Ortuzar, Chen Li, and Zeyu Gao
Accepted as a NeurIPS 2026 poster: official conference program event 149764 matches the exact title and 12 authors, checked 2026-10-08. Final proceedings were not verified; review uses arXiv v1 (2026-09-28). Official method code is available under a noncommercial research license, but does not include the WSI feature-extraction pipeline. Tissue-tile pixel size, extraction magnification/MPP, and image preprocessing remain unreported in the inspected paper and release.
Official artifacts: Reviewed manuscript and supplement (v1) · Author publication and acceptance statement · Official NeurIPS 2026 poster · Official method implementation (noncommercial research)
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- L3
MOOZY: A Patient-First Foundation Model for Computational Pathology
Yousef Kotp, Vincent Quoc-Huy Trinh, Christopher Pal, and Mahdi S. Hosseini
ECCV 2026 poster presentation is now verified by the official conference program (poster 4955), superseding author-only acceptance evidence. A Springer version of record was not verified on 2026-09-12. The program lists Yousef Hassan whereas the retained arXiv record lists Yousef Kotp; preserve the declared manuscript authorship rather than silently replacing it.
Official artifacts: Project · Code · Model · Official ECCV 2026 poster · Conference manuscript
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- L3
Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models
Tim Lenz, Maurice Heide, Marco Gustav, Nic G. Reitsam, and Jakob Nikolas Kather
Official MICCAI satellite proceedings confirm the 2nd Efficient Medical AI workshop (EMA), not the main MICCAI track. LNCS volume 17265 is listed; DOI and pagination remain pending on the checked record. Full 11-page accepted manuscript inspected. Teachers and students use 2.0 µm/px (5×); this is capacity compression, not a demonstrated 20×→5× transfer method. Raspberry Pi timings concern individual encoder forward passes, not complete WSI processing.
Official artifacts: Official workshop accepted manuscript · Author implementation and documented scope
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- L3
MUSE: Harnessing Precise and Diverse Semantics for Few-Shot Whole Slide Image Classification
Jiahao Xu et al.
Official artifacts: Code
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- L3
MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models
Ashshak Sharifdeen, Shihab Aaqil Ahamed, Ufaq Khan, Muhammad Akhtar Munir Sujair Ibrahim, Mohamed Rafeek Mareer Ahamed, Yutong Xie, Imran Razzak, and Muhammad Haris Khan
The arXiv record reports acceptance to Findings of EMNLP 2026. No final ACL Anthology record was available when checked on 30 August 2026, so claims were verified against arXiv v1 and the official repository.
Official artifacts: Official implementation
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- L3
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
Duong M. Nguyen, Trong Nghia Hoang, Hang Thi Nguyen, Thanh Trung Huynh, Phi Le Nguyen, and Minh N. Do
Acceptance independently verified by NeurIPS 2026 poster 153235 and author repository. Review uses arXiv:2610.00678v1, submitted 2026-09-30. Repository contained only a README on 2026-10-08. Feature condensation at 20× is not low-magnification encoder distillation; Table 1 tests on full feature bags.
Official artifacts: Official NeurIPS 2026 program · Versioned full manuscript · Author repository (README only at audit)
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- L3
On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation
Ruben T. Lucassen et al.
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- L3
One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training
Agnieszka Florkowska, Henning Müller, and Marek Wodzinski
arXiv v1 submitted 10 August 2026; no peer-reviewed venue was verified on 2026-09-02. A single CNN is conditioned on physical pixel size and trained over a continuous magnification range; each prediction still consumes one patch scale and the method is neither MIL nor knowledge distillation.
Official artifacts: Official code
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- L3
PAGET: Hierarchical Multi-Teacher Knowledge Distillation for Comprehensive Tumor Microenvironment Segmentation
Daisuke Komura, Maki Takao, Mieko Ochi, Takumi Onoyama, Hiroto Katoh, Hiroyuki Abe, Hiroyuki Sano, Teppei Konishi, Toshio Kumasaka, Tomoyuki Yokose, Yohei Miyagi, Tetsuo Ushiku, and Shumpei Ishikawa
Peer-reviewed MIDL 2026 proceedings, PMLR 315:256–279; official record verified 2026-10-02.
Official artifacts: Official proceedings PDF · Official implementation
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- L2–L3
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
Han Li, Jingsong Liu, Ayako Ura, Junlin Hou, Zhengyang Xu, Azar Kazemi, Oskar Thaeter, Christian Grashei, Fabian Gülhan, Reza Nasirigerdeh, Xun Ma, Rui Yan, Hao Chen, S. Kevin Zhou, Nassir Navab, Carolin Mogler, and Peter Schüffler
arXiv preprint submitted 4 July 2026 and revised as v2 on 17 July 2026; no peer-reviewed version was verified on 20 August 2026.
Official artifacts: Official gated dataset card
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- L3
PANDA: Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimer's MRI and TCGA Pathology
Sheethal Bhat, Mahfuzur Rahman Chowdhury, Paula Andrea Perez-Toro, Stephan Wunderlich, Rose Dawn Bharat, Siming Bayer, and Andreas Maier
arXiv v1 preprint submitted 26 August 2026. The manuscript promises code upon acceptance, but no public implementation was available at the review cutoff.
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- L3
Patch-level phenotype identification via weakly supervised neuron selection in sparse autoencoders for CLIP-derived pathology embeddings
Keita Tamura, Yao-zhong Zhang, Yohei Okubo, and Seiya Imoto
PSB 2026 volume 31, pages 708–721, with source supplement checked. Neuron discovery uses slide labels, but the reported localization thresholds use patch-level validation annotations. Single-neuron localization does not preserve all multi-feature slide-classification performance.
Official artifacts: Official PSB proceedings PDF · Author implementation and supplement
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- L3
PATH-DISSECT: Interpreting Concept Representation and Classifier Reliance in Pathology Foundation Models with Sparse Autoencoders
Mayur Parvatikar, Xingjian Li, and Min Xu
Official MICCAI 2026 Satellite Events page supplies the 11-page camera-ready; Springer DOI and final pages were pending at inspection. This is MI4MedFM workshop work on tissue patches, not main-conference or patient-level WSI outcome validation. Causal claims concern interventions on a frozen encoder plus linear readout, not biological causation.
Official artifacts: Official camera-ready PDF · Author implementation · Workshop OpenReview record
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- L3
PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image
Dankai Liao et al.
Submitted 2026-07-21; retain preprint status and do not treat this benchmark as an established standard.
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- L3
PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology
Fengchun Liu et al.
Official artifacts: Code
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- L3
PathFound: An agentic multimodal model activating evidence-seeking pathological diagnosis
Shengyi Hua et al.
Official artifacts: Code
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- L3
PathGLS: Evaluating Pathology Vision-Language Models without Ground Truth through Multi-Dimensional Consistency
Minbing Chen et al.
Official artifacts: Code
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- L3
Patho-AgenticRAG: Towards Multimodal Agentic Retrieval-Augmented Generation for Pathology VLMs via Reinforcement Learning
Wenchuan Zhang et al.
Official artifacts: Code
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- L3
Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner
Wenchuan Zhang et al.
Official artifacts: Code and models
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- L3
Pathologist Attention–Aligned Report Generation for Prostate Histopathology
Ruoyu Xue, Suryakant Singh, Souradeep Chakraborty, Pierre Marza, Oksana Yaskiv, Constantin Friedman, Natallia Sheuka, Paul Friedman, Bharat Ramlal, Beatrice Knudsen, Rajarsi Gupta, Joel Saltz, Prateek Prasanna, Gregory Zelinsky, and Dimitris Samaras
Official MICCAI 2026 proceedings record and accepted-version open-access PDF verified 2026-10-02; Springer DOI and pagination remain pending.
Official artifacts: Code and attention data · Official MICCAI 2026 proceedings · Official accepted-version PDF
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- L3
Pathology-CoT: Learning Visual Chain-of-Thought Agents from Expert Whole-Slide Image Diagnosis Behaviour
Sheng Wang et al.
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- L3
PathPilot: An Adaptive Question-Conditioned Multi-Magnification Navigation Agent for Whole-Slide Image Analysis
Zhengyang Xu, Han Li, Jingsong Liu, Ayako Ura, Shihui Zu, Shaohua Kevin Zhou, Nassir Navab, and Peter Schüffler
arXiv:2610.16734v1, submitted 12 October 2026; manuscript says under review, with no independently verified acceptance. Full 24-page public manuscript inspected; referenced supplementary material was not attached or independently obtained. Code is promised on publication, not a verified release. Adaptive thumbnail/1.25×/2.5×/5×/10× exploration is not knowledge distillation or low-resolution-only inference. QA scores use a language-model judge; deterministic repeated runs do not establish population uncertainty.
Official artifacts: Inspected arXiv v1 manuscript
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- L3
PathReportEval: A Systematic Benchmark for Pathology Report Generation
Suryakant Singh et al.
Submitted 2026-07-20; retain preprint status pending independent use and a version of record.
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- L3
Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation With Nested Embeddings
Christian Grashei, Christian Brechenmacher, Rao Muhammad Umer, Jingsong Liu, Carsten Marr, Peter J. Schüffler, and Ewa Szczurek
ECCV 2026 poster status reverified against the official complete program (poster 4454). No oral or spotlight designation is recorded. A Springer version of record was not verified on 2026-09-12; the existing review retains its declared manuscript revision.
Official artifacts: Official ECCV record · Official model · Conference manuscript
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- L3
PathView-Bench: Can Multimodal Large Language Models Achieve Fine-grained Multiscale Understanding of Pathology Images?
Zongyi Chen, Yu Liang, Jie Lin, and Liansheng Wang
Acceptance independently verified 2026-10-08: official NeurIPS poster 139172 exactly matches the title and four authors and links OpenReview gZJHNK7GHw. The scientific review remains tied to arXiv:2607.28318v1 and its 2026-08-08 full-text audit; this metadata refresh did not re-audit code or dataset availability.
Official artifacts: Official NeurIPS 2026 acceptance record
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- L3
Pathway-Structured Privileged Distillation for Deployable Computational Pathology
Yongxin Guo, Hao Lu, Onur C. Koyun, Zhengjie Zhu, Muhammet F. Demir, and Metin N. Gurcan
arXiv v1 (2026-06-01) checked 2026-10-02; no conference acceptance verified. Paired RNA is training-only, while WSI is used at deployment. Pathway-memory readouts are not independently validated molecular measurements.
Official artifacts: Versioned manuscript
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- L3
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
Mingxi Fu, Jiawen Li, Renao Yan, Jiali Hu, Qiehe Sun, Tian Guan, and Yonghong He
arXiv v1 (2026-07-16) checked 2026-10-02. Six cohorts cover 15 tasks, not 15 independent datasets. Official code and pretrained aggregator checkpoints are listed; experiments were not rerun. This transfers slide embeddings into MIL initialization, not 20×-to-5× acquisition.
Official artifacts: Versioned manuscript · Official implementation and pretrained MIL weights
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- L3
RACR-MIL: Rank-aware contextual reasoning for weakly supervised grading of squamous cell carcinoma using whole slide images
Anirudh Choudhary, Mosbah Aouad, Krishnakant Saboo, Angelina Hwang, Jacob Kechter, Blake Bordeaux, Puneet Bhullar, David DiCaudo, Steven Nelson, Nneka Comfere, Emma Johnson, Olayemi Sokumbi, Jason Sluzevich, Leah Swanson, Dennis Murphree, Aaron Mangold, and Ravishankar Iyer
The complete arXiv v3 manuscript identifies Medical Image Analysis as the journal and the abstract record reports acceptance; a final DOI or journal issue record was not yet available when checked on 30 August 2026.
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Recommendation Statement for the Validation, Implementation, and Clinical Application of Artificial Intelligence Within a Clinical Laboratory from the Digital Pathology Association
Nathan Silberman et al.
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Reliable Benchmarking of Artifact Detection in Computational Pathology: A Reproducibility and Uncertainty Analysis
Konstantinos Moutselos and Ilias Maglogiannis
arXiv v1 submitted 31 August 2026. The authors released reproducibility deposits on Zenodo; no peer-reviewed record was verified on 2 September 2026.
Official artifacts: Official reproducibility deposit · Referenced benchmark artifact deposit
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Revisiting foundation models for cell instance segmentation
Anwai Archit and Constantin Pape
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Robustifying pathology foundation models via fine-tuning
Alexandre Filiot, Oskar Thaeter, Benoit Schmauch, and Lionel Guillou
Official artifacts: Released models
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SAM-Cluster and SAM-LP: Structure-Aware Evaluation Metrics for Selecting WSI Feature Extractors Without MIL Training
Juhyeon Kim, Paul Ssemakula, Chanjae Song, and Mun Yong Yi
Official MICCAI 2026 open-access proceedings version. Springer DOI and final page range were not listed at the audit date.
Official artifacts: Official accepted-version PDF · Official implementation and benchmark results
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Scaling Whole-Slide Pathology Foundation Model Pretraining with Billions Off-the-Shelf Tokens
Honglin Li, Zhongyi Shui, Chenglu Zhu, and Lin Yang
ECCV 2026 acceptance and the manuscript are verified from the official program. No Springer version of record or author-owned public code/checkpoint was verified. Section 4.1 explicitly combines HistAI with about 10,000 private WSIs; do not describe pretraining as public-only. PathTokScale and PathTokenScaling both occur in the manuscript.
Official artifacts: Official ECCV 2026 Poster
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sCellST predicts single-cell gene expression from H&E images
Loïc Chadoutaud et al.
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Spatial proteomics guided by H&E-based AI reveals recurrence-risk niches in triple-negative breast cancer
Yesung Cho et al.
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Streamline Pathology Foundation Model by Cross-Magnification Distillation
Ziyu Su, Abdul Rehman Akbar, Usama Sajjad, Anil V. Parwani, and Muhammad Khalid Khan Niazi
Journal record verified through publisher metadata, Crossref and PubMed PMID 42874158 on 26 October 2026: electronic publication 10 October 2026, assigned to volume 115 (January 2027), article 104352. The stable ID retains the 2025 preprint year. Journal full text was unavailable; detailed scientific claims and visuals remain explicitly tied to the previously inspected arXiv v3, not asserted to match the journal version. The journal abstract describes seven tasks and encoder-level throughput, not an end-to-end speedup.
Official artifacts: Inspected author manuscript (v3) · PubMed journal metadata and abstract · Official code · Official model
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SurvMILKD: A Weakly Supervised Survival Analysis Framework for Multi-Teacher Knowledge Distillation using Pathology Foundation Models
Mayur Mallya, Ali Khajegili Mirabadi, Hossein Farahani, and Ali Bashashati
The official ECCV 2026 poster and conference-hosted manuscript verify acceptance; a Springer version of record was not verified. The official repository was a code-coming-soon placeholder on 2026-09-12. All foundation encoders remain frozen: task-specific MIL adapters and translation modules learn, not the UNI backbone.
Official artifacts: Official ECCV 2026 Poster · Official repository — code pending
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TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song, Yosep Chong, Kwangil Yim, and Jin Tae Kwak
Official ECCV 2026 poster and conference-hosted manuscript verify acceptance; no Springer version of record was verified. Author spelling follows the paper rather than the abbreviated program entry. Output is coarse/fine diagnostic label text, not full clinical reports. GastWSI is private.
Official artifacts: Official ECCV 2026 Poster · Official implementation · arXiv manuscript
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TopoSlide: Topologically-Informed Histopathology Whole Slide Image Representation Learning
Shahira Abousamra, Asmita Sood, and Sylvia Plevritis
Official artifacts: Code and models
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Towards Robust Foundation Models for Digital Pathology
Jonah Kömen et al.
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TRACE: Your Diffusion Model Is Secretly an Instance Edge Detector
Sanghyun Jo, Ziseok Lee, Wooyeol Lee, Jonghyun Choi, Jaesik Park, Kyungsu Kim
Official ICLR 2026 oral and full proceedings manuscript checked. Diffusion-boundary self-distillation improves selected natural-image segmentation but degrades MoNuSeg/TNBC cell segmentation. One-pass speed is edge extraction only, with the large backbone retained. No WSI or cross-magnification validation; official code/checkpoints remain promised rather than released at the checked revision.
Official artifacts: OpenReview · Official project · Official repository — release pending
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Training-Free Out-of-Distribution Detection for Pathology Whole-Slide Images
Sabri Mustafa Kahya et al.
The arXiv record describes the manuscript as under review; this catalog retains preprint status.
Official artifacts: Official code
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Turning Pre-Trained Vision Transformers into End-to-End Histopathology Whole Slide Image Models for Survival Prediction
Jiawen Li, Jiali Hu, Xitong Ling, Renao Yan, Yuxuan Chen, Tian Guan, and Yonghong He
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Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion
Yuxiang Xiao, Yang Hu, Bin Li, Tianyang Zhang, Zexi Li, Huazhu Fu, Jens Rittscher, and Kaixiang Yang
Official artifacts: Official code
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VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology
Luca L. Weishaupt, Simone de Brot, Javier Asin, Llorenç Grau-Roma, Nic G. Reitsam, Andrew H. Song, Dongmin Bang, Stefan T. Kaluziak, Long Phi Le, Jakob Nikolas Kather, Faisal Mahmood, and Guillaume Jaume
Acceptance independently verified 2026-10-08 through official NeurIPS poster 139742: exact title and matching 12-author list, linked OpenReview 7986DZ9rPe. Scientific review and artifact checks remain tied to arXiv:2608.26382v1 and the 2026-08-30 audit, not a refreshed full-text/code/data inspection. VIPER evaluates rat toxicologic regions rather than whole-slide images; it is an ROI benchmark, not a WSI benchmark.
Official artifacts: Official evaluation code and documentation · Official benchmark dataset · Official NeurIPS 2026 acceptance record
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What Carries the Signal in Pathology Foundation-Model Atlases? A Patient-Level Controlled Benchmark in Breast Cancer
Chimdi Walter Ndubuisi
Official artifacts: OpenTME data
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What Makes High-Magnification Knowledge Transferable? A Study of Cross-Resolution Distillation in Whole-Slide Imaging
Zhiyuan Yang, Jiahao Cheng, and Mahdi S. Hosseini
Public arXiv v2 (2026-10-01) verified 2026-10-02. The authors state ICLR 2027 under review; no acceptance inferred. Only public source material was used. A diagnostic study of target utility, predictability and downstream benefit, not a new universally superior distillation architecture.
Official artifacts: Reviewed manuscript revision
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Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols
Tiffanie Godelaine, Maxime Zanella, Karim El Khoury, Benoit Macq, and Christophe De Vleeschouwer
arXiv v1 submitted 31 August 2026; the manuscript title page says it was submitted on 29 July 2026. Code and data instructions were released, but no peer-reviewed record was verified on 2 September 2026.
Official artifacts: Official implementation and data instructions
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WILSON - a pathology foundation model framework for patient-level analysis and diagnostic text generation
Saghir Alfasly, Wataru Uegami, Sobhan Hemati, Wenchao Han, Xiaojia Tang, Kevin Thompson, Daniel Stone, Ghazal Alabtah, Saba Yasir, Michael R. Lucas, Eric W. Klee, Cheryl L. Willman, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, and H. R. Tizhoosh
arXiv v1 submitted 2026-09-20. No peer-reviewed record was verified. The manuscript says model release is planned after publication and institutional clearance; Mayo training WSIs/reports are not publicly downloadable.
Official artifacts: Full manuscript and supplementary material
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2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification
Jingwei Zhang, Anh Tien Nguyen, Xi Han, Vincent Quoc-Huy Trinh, Hong Qin, Dimitris Samaras, and Mahdi S. Hosseini
Official artifacts: Code
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A Vision–Language Foundation Model for Precision Oncology
Jinxi Xiang et al.
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A visual-omics foundation model to bridge histopathology with spatial transcriptomics
Weiqing Chen et al.
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AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification
Yunlong Zhang, Honglin Li, Yuxuan Sun, Zhongyi Shui, Jingxiong Li, Chenglu Zhu, and Lin Yang
Official artifacts: Official code
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Benchmarking foundation models as feature extractors for weakly supervised computational pathology
Neidlinger et al.
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CellSAM: a foundation model for cell segmentation
Markus Marks et al.
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CEMIL: Contextual Attention Based Efficient Weakly Supervised Approach for Histopathology Image Classification
Tawsifur Rahman, Alexander S. Baras, and Rama Chellappa
Peer-reviewed WACV 2025 proceedings paper, pages 4248–4257.
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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology
Yuxuan Sun et al.
Official artifacts: Code
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CPathAgent: An Agent-Based Foundation Model for Interpretable High-Resolution Pathology Image Analysis Mimicking Pathologists’ Diagnostic Logic
Yuxuan Sun et al.
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Distilling Foundation Models for Robust and Efficient Models in Digital Pathology
Alexandre Filiot, Nicolas Dop, Oussama Tchita, Auriane Riou, Rémy Dubois, Thomas Peeters, Daria Valter, Marin Scalbert, Charlie Saillard, Geneviève Robin, and Antoine Olivier
Peer-reviewed MICCAI 2025 proceedings paper.
Official artifacts: Official model · Official robustness benchmark
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Do Multiple Instance Learning Models Transfer?
Daniel Shao et al.
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Evidence-based diagnostic reasoning with multi-agent copilot for human pathology
Luca L. Weishaupt et al.
First submitted in 2025 and revised in 2026; no version of record was located by 2026-07-27.
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EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision
Myeongjang Pyeon et al.
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EXAONE Path 2.5: Pathology Foundation Model with Multi-Omics Alignment
Juseung Yun, Sunwoo Yu, Sumin Ha, Jonghyun Kim, Janghyeon Lee, Jongseong Jang, and Soonyoung Lee
Official artifacts: Model
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F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation
Man M. Ho, Shikha Dubey, Yosep Chong, Beatrice Knudsen, and Tolga Tasdizen
Official artifacts: Author preprint · Official project page
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Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning
Jiuyang Dong et al.
Official artifacts: Code
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FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
Guo et al.
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FUTURE-AI: International Consensus Guideline for Trustworthy and Deployable Artificial Intelligence in Healthcare
Lekadir et al.
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Official artifacts: Code
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Generating Dermatopathology Reports from Gigapixel Whole Slide Images with HistoGPT
Manuel Tran et al.
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Good machine learning practice for medical device development: Guiding principles
International Medical Device Regulators Forum
IMDRF/AIML WG/N88 FINAL:2025, finalized 2025-01-29.
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HARE: an entity and relation centric evaluation framework for histopathology reports
Yunsoo Kim et al.
Official artifacts: Code
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HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving
Farchan Hakim Raswa, Chun-Shien Lu, and Jia-Ching Wang
Peer-reviewed CVPR 2025 proceedings paper, pages 30251–30260.
Official artifacts: Official project · Official code
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Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial
Jay Jasti et al.
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Historical Report Guided Bi-modal Concurrent Learning for Pathology Report Generation
Ling Zhang et al.
Official artifacts: Code
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Human Visual Attention-Inspired Knowledge Distillation Underlying Interpretable Computational Pathology
Muzhou Yu, Zihan Zhong, Xingang Zhou, Yuekun Wang, Tingyu Liang, Jiamin Chen, Hongmin Huang, Junhan Zhou, Dachun Zhao, Bo Lei, Yu Wang, Wenbin Ma, and Kaisheng Ma
Peer-reviewed open-access journal article 15:42124. Low- and high-magnification language motivates the design, but HVisKD transfers sample- and region-relation features between teacher and student given the same pathology patches; it is not cross-magnification distillation.
Official artifacts: Open-access article · Official code
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Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images
Mahmut S. Gokmen, Mitchell A. Klusty, Peter T. Nelson, Allison M. Neltner, Sen-Ching Samson Cheung, Thomas M. Pearce, David A. Gutman, Brittany N. Dugger, Devavrat S. Bisht, Margaret E. Flanagan, and V. K. Cody Bumgardner
Preprint; arXiv comments report submission to the AMIA 2026 Informatics Summit, not acceptance.
Official artifacts: Official model
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Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions
U.S. Food and Drug Administration
Final U.S. FDA guidance issued August 2025; jurisdiction-specific.
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ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Adaptation
Ramanathan et al.
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Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathology
Qibin Zhang, Xinyu Hao, Qiao Chen, Rui Xu, Fengyu Cong, Cheng Lu, and Hongming Xu
Official artifacts: Official code
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Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Shahad Albastaki et al.
Official artifacts: Code
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PathDiff: Histopathology Image Synthesis with Unpaired Text and Mask Conditions
Mahesh Bhosale, Abdul Wasi, Yuanhao Zhai, Yunjie Tian, Samuel Border, Nan Xi, Pinaki Sarder, Junsong Yuan, David Doermann, and Xuan Gong
Official artifacts: Official code
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PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology
Fatemeh Ghezloo et al.
Official artifacts: Project, code, and data
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PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
Yuxuan Sun et al.
Official artifacts: Data, code, and models
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PatHME: Hierarchical Multi-Expert Knowledge Distillation for Whole Slide Image Analysis
Jing Wei Tan, Gangsu Kim, and Won-Ki Jeong
Peer-reviewed journal article 13:163246–163257.
Official artifacts: Official code
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PathoCellBench: A Comprehensive Benchmark for Cell Phenotyping
Jérôme Lüscher et al.
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PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology
Yating Huang et al.
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Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions
Ruben T. Lucassen et al.
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PathVG: A New Benchmark and Dataset for Pathology Visual Grounding
Chunlin Zhong et al.
Official artifacts: Code and data
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Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis
Xiaoyi Ji et al.
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PolyPath: Adapting a Large Multimodal Model for Multislide Pathology Report Generation
Faruk Ahmed et al.
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PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue
Eugene Vorontsov et al.
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Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
Susu Sun, Dominique van Midden, Geert Litjens, and Christian F. Baumgartner
Official MICCAI 2025 record and 11-page accepted manuscript checked. PANDA is evaluated as binary tumor classification in this study, not the original six-class ISUP grading task. Artifact concepts are masked before MIL retraining; this is not a universal AUC improvement.
Official artifacts: Official MICCAI open-access record · Official accepted manuscript · Author implementation · arXiv manuscript
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Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching
Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Wengong Jin, and Rex Ying
Official artifacts: Official code
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Segment Anything for Histopathology
Griebel et al.
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Segment Anything for Microscopy
Anwai Archit et al.
Published online in 2024 and assigned to the 2025 volume.
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Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer
Bojing Liu et al.
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Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Saghir Alfasly, Wataru Uegami, Md Enamul Hoq, Ghazal Alabtah, and Hamid Tizhoosh
Official artifacts: Author preprint
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SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
Ying Chen et al.
Official artifacts: Code, model, and data
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STPath: A Generative Foundation Model for Integrating Spatial Transcriptomics and Whole-Slide Images
Huang et al.
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Systematic inference of super-resolution cell spatial profiles from histology images
Peng Zhang et al.
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The STARD-AI Reporting Guideline for Diagnostic Accuracy Studies Using Artificial Intelligence
Sounderajah et al.
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The TRIPOD-LLM reporting guideline for studies using large language models
Jack Gallifant et al.
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TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
Meilong Xu, Saumya Gupta, Xiaoling Hu, Chen Li, Shahira Abousamra, Dimitris Samaras, Prateek Prasanna, and Chao Chen
Official artifacts: Official code
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Unsupervised Anomaly Detection on Preclinical Liver H&E Whole Slide Images Using Graph-Based Feature Distillation
Lin Li, Lillie Shelton, Thomas Forest, Kyathanahalli Janardhan, Tiffany Jenkins, Michael J. Napolitano, Roujia Wang, David Leigh, Tosha Shah, Grady Earl Carlson, Rajath Soans, and Antong Chen
Peer-reviewed MICCAI 2025 proceedings paper; evaluated on preclinical rat-liver H&E WSIs. The author project page requests email contact for code access and showed code as coming soon when checked on 2026-08-23.
Official artifacts: Author project and code-access page
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Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
Tim Lenz, Peter Neidlinger, Marta Ligero, Georg Wölflein, Marko van Treeck, and Jakob N. Kather
Official artifacts: Code
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A foundation model for clinical-grade computational pathology and rare cancers detection
Eugene Vorontsov et al.
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A Graph-Based Multi-Scale Approach With Knowledge Distillation for WSI Classification
Gianpaolo Bontempo, Federico Bolelli, Angelo Porrello, Simone Calderara, and Elisa Ficarra
Peer-reviewed journal article 43(4):1412–1421; journal extension of DAS-MIL.
Official artifacts: Official code
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A Multimodal Generative AI Copilot for Human Pathology
Ming Y. Lu et al.
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A Visual-Language Foundation Model for Computational Pathology
Ming Y. Lu et al.
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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Graham et al.
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CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
Sajid Javed et al.
Official artifacts: Project
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Diffusion-Based Generation of Histopathological Whole Slide Images at a Gigapixel Scale
Robert Harb, Thomas Pock, and Heimo Müller
Official artifacts: arXiv
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DINOv2: Learning Robust Visual Features without Supervision
Maxime Oquab et al.
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EXAONEPath 1.0 Patch-level Foundation Model for Pathology
Juseung Yun, Yi Hu, Jinhyung Kim, Jongseong Jang, and Soonyoung Lee
Official artifacts: Code and model
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Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
Wenhao Tang et al.
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HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Zhengrui Guo et al.
Official artifacts: Code
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Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
Daiwei Zhang et al.
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Learning biologically relevant features in a pathology foundation model using sparse autoencoders
Nhat Minh Le, Ciyue Shen, Neel Patel, Chintan Shah, Darpan Sanghavi, Blake Martin, Alfred Eng, Daniel Shenker, Harshith Padigela, Raymond Biju, Syed Ashar Javed, Jennifer Hipp, John Abel, Harsha Pokkalla, Sean Grullon, and Dinkar Juyal
Review uses arXiv:2407.10785v3, including its supplement. A later NeurIPS 2025 Imageomics workshop paper has the title Evaluating the Utility of Sparse Autoencoders for Interpreting a Pathology Foundation Model; its inaccessible full PDF was not used to replace this version's claims or publication status.
Official artifacts: Versioned full manuscript · Authors' later Imageomics workshop publication
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MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology
Shu Yang et al.
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Modeling Dense Multimodal Interactions between Biological Pathways and Histology for Survival Prediction
Jaume et al.
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Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Andrew H. Song et al.
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PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology
Yuxuan Sun et al.
Official artifacts: Code
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PathLDM: Text Conditioned Latent Diffusion Model for Histopathology
Srikar Yellapragada, Alexandros Graikos, Prateek Prasanna, Tahsin Kurc, Joel Saltz, and Dimitris Samaras
Official artifacts: Author preprint · Official code
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PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
Yuxuan Sun et al.
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Phikon-v2, A large and public feature extractor for biomarker prediction
Guillaume Filiot et al.
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PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology
George Shaikovski et al.
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Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
Mehmet Saygin Seyfioglu et al.
Official artifacts: Project
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Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping
Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, and Nishant Ravikumar
Peer-reviewed ISBI 2024 proceedings paper.
Official artifacts: Author manuscript
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Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
Honglin Li et al.
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Rotation-Agnostic Image Representation Learning for Digital Pathology
Saghir Alfasly et al.
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SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification
Tong Shu, Jun Shi, Dongdong Sun, Zhiguo Jiang, and Yushan Zheng
Official artifacts: Official code
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SpatialData: an open and universal data framework for spatial omics
Luca Marconato et al.
Published online in 2024 and assigned to volume 22 in 2025.
Official artifacts: Documentation
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Towards a general-purpose foundation model for computational pathology
Richard J. Chen et al.
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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
Collins et al.
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A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin, Jochen Lennerz, Stephen Yip, Hossein Farahani, and Ali Bashashati
Official artifacts: Author preprint
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A Visual–Language Foundation Model for Pathology Image Analysis Using Medical Twitter
Zhi Huang et al.
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Attention to Detail: Inter-Resolution Knowledge Distillation
Rocío del Amor, Julio Silva-Rodríguez, Adrián Colomer, and Valery Naranjo
Peer-reviewed EUSIPCO 2023 proceedings paper, pages 985–989.
Official artifacts: Open proceedings paper · Official code
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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Mingu Kang et al.
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BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
Sheng Zhang et al.
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Computational Pathology at Health System Scale — Self-Supervised Foundation Models from Three Billion Images
Gabriele Campanella et al.
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Deep Multi-Magnification Similarity Learning for Histopathological Image Classification
Songhui Diao, Weiren Luo, Jiaxin Hou, Ricardo Lambo, Hamas A. AL-kuhali, Hanqing Zhao, Yinli Tian, Yaoqin Xie, Nazar Zaki, and Wenjian Qin
Peer-reviewed journal article 27(3):1535–1545. DMSL learns paired same-field-of-view patch features at two magnifications and retains both magnification branches at inference; its reported comparison metrics are patch-level, not a WSI endpoint.
Official artifacts: PubMed record
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Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance
Milda Pocevičiūtė, Gabriel Eilertsen, Stina Garvin, and Claes Lundström
Peer-reviewed MICCAI 2023 proceedings paper, pages 157–167; its arXiv upload appeared in 2024 and does not change the venue year.
Official artifacts: MICCAI paper record · Author manuscript
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DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
Marco Aversa, Gabriel Nobis, Miriam Hägele, Kai Standvoss, Mihaela Chirica, Roderick Murray-Smith, Ahmed M. Alaa, Lukas Ruff, Daniela Ivanova, Wojciech Samek, Frederick Klauschen, Bruno Sanguinetti, and Luis Oala
Official artifacts: Author preprint · Official code
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Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Lai et al.
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Improving Representation Learning for Histopathologic Images with Cluster Constraints
Wanyi Wu et al.
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Knowledge Distillation in Histology Landscape by Multi-Layer Features Supervision
Sajid Javed, Arif Mahmood, Talha Qaiser, and Naoufel Werghi
Peer-reviewed journal article 27(4):2037–2046.
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Mitosis domain generalization in histopathology images — The MIDOG challenge
Marc Aubreville et al.
Peer-reviewed Medical Image Analysis article 84:102699; published online 23 November 2022 and assigned to the 2023 volume.
Official artifacts: Author manuscript · Official challenge and data · Reference container
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Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
Wenhao Tang et al.
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Next-Generation Morphometry for pathomics-data mining in histopathology
David L. Hölscher et al.
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Pacpaint: A Histology-Based Deep Learning Model Uncovers the Extensive Intratumor Molecular Heterogeneity of Pancreatic Adenocarcinoma
Saillard et al.
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Quilt-1M: One Million Image-Text Pairs for Histopathology
Wisdom Ikezogwo et al.
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Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
Guillaume Filiot et al.
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Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
Mahmoud Assran et al.
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Sigmoid Loss for Language Image Pre-Training
Xiaohua Zhai et al.
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Structured State Space Models for Multiple Instance Learning in Digital Pathology
Leo Fillioux et al.
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Visual Instruction Tuning
Haotian Liu et al.
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Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
Ming Y. Lu et al.
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BEiT: BERT Pre-Training of Image Transformers
Hangbo Bao et al.
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CoCa: Contrastive Captioners are Image-Text Foundation Models
Jiahui Yu et al.
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ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images
Jiawei Yang, Hanbo Chen, Yuan Liang, Junzhou Huang, Lei He, and Jianhua Yao
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Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani, and Orcun Goksel
Peer-reviewed ECCV 2022 proceedings paper.
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DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
Hongrun Zhang et al.
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Federated learning for computational pathology on gigapixel whole slide images
Ming Y. Lu, Richard J. Chen, Dehan Kong, Jana Lipkova, Rajendra Singh, Drew F. K. Williamson, Tiffany Y. Chen, and Faisal Mahmood
Peer-reviewed Medical Image Analysis article 76:102298; DOI 10.1016/j.media.2021.102298.
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High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach et al.
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iBOT: Image BERT Pre-Training with Online Tokenizer
Jinghao Zhou et al.
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Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
Karin Stacke et al.
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Masked Autoencoders Are Scalable Vision Learners
Kaiming He et al.
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Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Richard J. Chen et al.
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Self supervised contrastive learning for digital histopathology
Ozan Ciga et al.
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Self-supervised driven consistency training for annotation efficient histopathology image analysis
C. L. Srinidhi et al.
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Transformer-based unsupervised contrastive learning for histopathological image classification
Xiyue Wang et al.
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Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology
Andrew J. Evans et al.
Published online in 2021 and in the 2022 journal issue; active CAP evidence-based guideline.
Official artifacts: CAP guideline page
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VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Adrien Bardes, Jean Ponce, and Yann LeCun
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Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar et al.
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Cellpose: A Generalist Algorithm for Cellular Segmentation
Stringer et al.
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Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron et al.
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Exploring Simple Siamese Representation Learning
Xinlei Chen and Kaiming He
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Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation
Rikiya Yamashita, Jin Long, Snikitha Banda, Jeanne Shen, and Daniel L. Rubin
Peer-reviewed journal article in IEEE Transactions on Medical Imaging 40(12):3945–3954; the linked arXiv manuscript is an author version.
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Learning Transferable Visual Models From Natural Language Supervision
Alec Radford et al.
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Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
Graham et al.
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Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
Chen et al.
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OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies
Josh Moore, Chris Allan, Sébastien Besson, Jean-Marie Burel, Erin Diel, David Gault, Kevin Kozlowski, Dominik Lindner, Melissa Linkert, Trevor Manz, Will Moore, Constantin Pape, Christian Tischer, and Jason R. Swedlow
Official artifacts: Version-of-record DOI · Official OME-NGFF specification
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Resolution-Based Distillation for Efficient Histology Image Classification
Joseph DiPalma, Arief A. Suriawinata, Laura J. Tafe, Lorenzo Torresani, and Saeed Hassanpour
Peer-reviewed journal article 119:102136.
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Self-Path: Self-Supervision for Classification of Pathology Images with Limited Annotations
Navid Alemi Koohbanani et al.
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Self-Supervised Representation Learning Using Visual Field Expansion on Digital Pathology
James Boyd et al.
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The Impact of Site-Specific Digital Histology Signatures on Deep Learning Model Accuracy and Bias
Howard et al.
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Towards Visual Question Answering on Pathology Images
Xuehai He et al.
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TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Zhuchen Shao et al.
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh et al.
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A Deep Learning Model to Predict RNA-Seq Expression of Tumours from Whole Slide Images
Schmauch et al.
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A Multi-Organ Nucleus Segmentation Challenge
Kumar et al.
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Bootstrap Your Own Latent
Jean-Bastien Grill et al.
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel
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Evaluation of Contrastive Predictive Coding for Histopathology Applications
Karin Stacke et al.
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Guidelines for Clinical Trial Protocols for Interventions Involving Artificial Intelligence: The SPIRIT-AI Extension
Cruz Rivera et al.
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HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Graham et al.
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Improved Baselines with Momentum Contrastive Learning
Xinlei Chen et al.
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Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He et al.
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NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopic Images
Alemi Koohbanani et al.
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PanNuke Dataset Extension, Insights and Baselines
Gamper et al.
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron et al.
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Gabriele Campanella et al.
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Deep Leakage from Gradients
Ligeng Zhu, Zhijian Liu, and Song Han
Official artifacts: Final proceedings PDF · Authors' implementation
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Deep Learning Can Predict Microsatellite Instability Directly from Histology in Gastrointestinal Cancer
Kather et al.
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Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
Yair Rivenson, Hongda Wang, Zhensong Wei, Kevin de Haan, Yibo Zhang, Yichen Wu, Harun Günaydın, Jonathan E. Zuckerman, Thomas Chong, Anthony E. Sisk, Lindsey M. Westbrook, W. Dean Wallace, and Aydogan Ozcan
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Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub Tomczak, and Max Welling
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Cell Detection with Star-Convex Polygons
Schmidt et al.
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Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images Using Deep Learning
Coudray et al.
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Deep Clustering for Unsupervised Learning of Visual Features
Mathilde Caron et al.
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Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer
Steiner et al.
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Implementing the DICOM Standard for Digital Pathology
Markus D. Herrmann, David A. Clunie, Andriy Fedorov, Sean W. Doyle, Steven Pieper, Veronica Klepeis, Long P. Le, George L. Mutter, David S. Milstone, Thomas J. Schultz, Ron Kikinis, Gopal K. Kotecha, David H. Hwang, Katherine P. Andriole, A. John Iafrate, James A. Brink, Giles W. Boland, Keith J. Dreyer, Mark Michalski, Jeffrey A. Golden, David N. Louis, and Jochen K. Lennerz
Official artifacts: Official DICOM pathology standard portal
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Representation Learning with Contrastive Predictive Coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals
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Unsupervised Representation Learning by Predicting Image Rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas
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Deep Sets
Manzil Zaheer et al.
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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Ehteshami Bejnordi et al.
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter
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Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Sirinukunwattana et al.
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Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros
Official artifacts: Official implementation
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth
Official artifacts: Version-of-record manuscript · DOI
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros
Official artifacts: Official implementation
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Deep Learning with Differential Privacy
Martín Abadi et al.
Official artifacts: Google differential privacy
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Domain-Adversarial Training of Neural Networks
Yaroslav Ganin et al.
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Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images
Vahadane et al.
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Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Mehdi Noroozi and Paolo Favaro
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox
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Unsupervised Visual Representation Learning by Context Prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros
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Generative Adversarial Nets
Ian Goodfellow et al.
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Solving the Multiple Instance Problem with Axis-Parallel Rectangles
Thomas G. Dietterich et al.
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No records match these filters.
How to use this registry
Section titled “How to use this registry”- Start with one dedicated chapter, because its paper order is pedagogical rather than a leaderboard.
- Use the filters here to compare adjacent methods by year, evidence state, or research route.
- Open My Library, select the same route, and add the papers you intend to read. Your reading status and notes stay only in your browser.
- Re-check the linked primary source before making a publication-status, artifact, performance, licensing, or clinical-use claim.
One paper can belong to several routes. MOOZY, for example, belongs to both foundation encoders and MIL/whole-slide learning because it learns slide representations and then aggregates multiple slides into a patient-case representation. It is not classified as a pathology VLM.
Coverage and ranking policy
Section titled “Coverage and ranking policy”This registry is a reviewed research map, not a global ranking. Route order expresses prerequisites and conceptual progression. The project does not rank papers by arXiv recency, citation count, venue prestige, or a single reported metric because those signals do not establish methodological quality or clinical relevance.
The daily arXiv Radar is the discovery layer. A radar hit enters this registry only after identity, scope, evidence state, route fit, and primary links have been checked.
Extending it
Section titled “Extending it”The catalog is appendable through dated data modules under
src/data/papers/computational-pathology/. A new audit module can add canonical
records or attach an existing paper to another route without duplicating it. See the
source repository’s literature maintenance protocol and paper-suggestion issue form
before proposing an update.