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Computational Pathology Reading Roadmap

Writing study: Advancing Image Classification with Discrete Diffusion Classification Modeling

← CVPR 2026 oral writing corpus

Omer Belhasin, Shelly Golan, Ran El-Yaniv, Michael Elad · Official CVPR 2026 oral record

Figure, table and section ledger

Editorial contribution type: Training, objectives & optimization (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF
11 physical pages
Before references / appendix
≈ 8.08 page-equivalents
Main figures / tables
4 / 1
Attached appendix figures / tables
0 / 0
Attached appendix span
Separate file recorded below; not in this PDF total
Before references / appendix: ≈ 73.45%References / intervening material: ≈ 26.55%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Extraction notes: No attached appendix boundary detected. Zero means no recognized caption, not independently verified absence.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 1.27 pp.11.55%
2. Problem Formulation2≈ 0.23 pp.2.09%
3. Related Work2≈ 0.52 pp.4.73%
4. Discrete Diffusion Classification Modeling3≈ 1.41 pp.12.82%
5. Simulating DiDiCM with Concrete Scores4≈ 1.65 pp.15%
6. Empirical Study6≈ 2.39 pp.21.73%
7. Concluding Remarks8≈ 0.29 pp.2.64%
References9≈ 2.92 pp.26.55%
All detected figure and table locations

Main text

Attached appendix

    No recognized caption labels.

    Separate source files—excluded from canonical-PDF totals

    A separate author version may repeat main text and references. Whole-file lengths and counts are not extra appendix material and are never added to the canonical PDF totals. Read the study's source scope for which sections received substantive reading versus layout inspection.

    Additional source 1

    Separate PDF (or ZIP containing it): whole-file layout of 7 physical pages; 2 detected figures and 5 tables. These may include overlapping main-text material.

    Located appendix only: starts on physical p.1; ≈ 6.94 page-equivalents, 2 figure labels and 5 table labels. This interval is distinct from the whole file.

    Detected headingPDF pageEstimated span
    Supplementary Material1≈ 0.02 pp.
    A. Background on Transition Matrices1≈ 1.82 pp.
    B. Ablation Study of Label Selection Strategies2≈ 0.73 pp.
    C. Visualizing DiDiCM Using Misclassified3≈ 0.2 pp.
    D. Empirical Study of DiDiRN Model Size3≈ 0.92 pp.
    E. Empirical Study on ImageNet-C4≈ 1.05 pp.
    F. Technical Training Details5≈ 0.9 pp.
    G. The Quality-Efficiency Tradeoff6≈ 1.29 pp.

    Argument and organization

    1. Problem and contribution

      Introduces uncertainty from corrupted inputs and limited training data as the setting where ordinary direct label prediction should be challenged.

    2. Argument sequence

      Moves from problem formulation through forward/reverse processes and training objective to probability-space and label-space implementations before experiments.

    3. Evidence and verification

      Table 1 crosses training fraction, resolution and augmentation recipe; Figure 4 separately plots accuracy against function evaluations.

    Problem, claim and contribution

    Introduces uncertainty from corrupted inputs and limited training data as the setting where ordinary direct label prediction should be challenged.

    Abstract and Section 1, p. 1 · Main paper · Additional source 2

    Argument and section order

    Moves from problem formulation through forward/reverse processes and training objective to probability-space and label-space implementations before experiments.

    Sections 2–6, pp. 2–7 · Main paper · Additional source 2

    Figures, captions and visual explanation

    Figure 2 pairs each image with a trajectory of its five leading class probabilities, turning iterative inference into an inspectable process rather than a black-box arrow.

    Figure 2, p. 6 · Main paper · Additional source 2

    Tables, comparisons and evidence

    Table 1 crosses training fraction, resolution and augmentation recipe; Figure 4 separately plots accuracy against function evaluations.

    Table 1 and Figure 4, p. 8 · Main paper · Additional source 2

    Appendix and reproducibility

    Misclassified examples and explicit quality–efficiency configurations accompany derivations, avoiding a success-only appendix.

    Supplement Appendices C and G, pp. 3–7 · Main paper · Additional source 2

    Clarity and writing conventions

    The results distinguish augmentation-policy comparisons from quality-versus-efficiency analysis using named run-in headings.

    Section 6.2, pp. 7–8 · Main paper · Additional source 2

    What to reuse—and what not to infer

    Transferable lesson: Show what each iteration changes, and evaluate the claimed uncertainty advantage under controlled data, resolution and augmentation settings.

    Boundary: Diffusion iterations and function evaluations add cost; improved classification accuracy alone is not evidence of lower deployment latency.

    Apply this lesson using the argument and evidence worksheet.

    Inspection record and sources

    Checked
    2026-09-26
    Reading status
    Writing-focused manuscript scan + selected visuals inspected
    Main text
    Writing-oriented scan throughout Sections 1–7, PDF pp. 1–8; references pp. 9–11: abstract/framing, section sequence, figure/table captions, evaluation organization and conclusions; not an exhaustive technical verification.
    Appendix / supplement
    Writing-oriented supplement scan: Supplement Appendices A–G, pp. 1–7: transition-rate background, label-selection ablations, misclassifications, scaling, corruption and quality/efficiency. Video/code assets were not executed or assessed.
    Visually inspected PDF pages
    6, 8

    This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.