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REVIEW 3 major objections 6 minor 56 references

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A model trained only on healthy colon tissue separates serrated lesions from conventional adenomas through confidence-score shifts on glands, matching the two recognized colorectal cancer pathways.

desk verdict ADPv2 is a solid, publicly released colon histology dataset, but the 'confirms two cancer pathways' claim reads stronger than the statistics support. read the letter →

arxiv 2507.05656 v2 pith:EDHWR4JE submitted 2025-07-08 eess.IV cs.CVcs.LGq-bio.QM

Zhiyuan Yang , Kai Li , Sophia Ghamoshi Ramandi , Patricia Brassard , Hakim Khellaf , Vincent Quoc-Huy Trinh , Jennifer Zhang , Lina Chen
show 4 more authors
Corwyn Rowsell Sonal Varma Kostas Plataniotis Mahdi S. Hosseini
This is my paper · ORCID
classification eess.IVcs.CVcs.LGq-bio.QM
keywords DeepLearningMultilabelRepresentationComputationalPathologyADPv2DatasetBiomarkerDiscoverySelf-SupervisedColorectalCancerColonHistopathology
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces ADPv2, a publicly available dataset of 20,004 image patches from healthy colon biopsies annotated with a hierarchical taxonomy of 32 histological tissue types across three levels, and trains a multilabel classifier on it using a two-stage procedure that reaches a mean average precision of 0.88. The central claim is that a model trained exclusively on healthy tissue can act as a probe for disease: applied to gland regions of polyps, its confidence scores shift in ways that separate serrated lesions (sessile serrated lesions and hyperplastic polyps) from conventional adenomas (tubular and tubulovillous adenomas). The authors interpret the shift patterns as statistical confirmation of the two established pathological pathways of colorectal cancer development and propose them as candidate image-based biomarkers. If the claim holds, biomarker discovery would no longer require large collections of annotated diseased tissue; a well-annotated baseline of normality and a confident model would suffice to expose disease signatures.

What carries the argument

The load-bearing mechanism is the confidence score distribution analysis: a model that has only seen healthy colon tissue is applied to pathologist-annotated regions of interest on diseased and normal slides, and the logit-transformed confidence scores for each histological tissue type are compared across disease groups with Welch's two-sample t-tests and Holm-Bonferroni correction. The premise is that morphological deviation from normal tissue lowers and reshapes the model's confidence, so the shift pattern itself is the candidate biomarker. The representations come from a two-stage pipeline: Barlow Twins self-supervised pretraining, which decorrelates features by driving the cross-correlation matrix of two augmented views toward the identity matrix, followed by fine-tuning with the asymmetric loss on 14 pruned labels using the VMamba visual state-space model, a token-scanning architecture whose gating mechanism keeps time and memory linear in the number of image tokens.

What would settle it

A decisive control would run the same RoI confidence analysis on healthy tissue only, comparing two groups that differ in stain or scanner rather than disease status (for example, HPS-stained versus H&E-stained normal colon biopsies): if a comparable leftward shift and TT-versus-SH separation appears, the biomarker signature tracks imaging domain rather than biology. A cheaper check is a permutation test that shuffles disease labels across RoI patches and recomputes the Welch t-statistics; if randomized groupings produce separations as large as t = 3.88, the reported pattern is not specific to the two carcinogenesis pathways.

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Extended reading notes

Core claim

The paper's central claim is that confidence-score distributions from a model trained only on healthy colon tissue carry disease-specific biological information. On gland areas, diseased patches shift the model's logit-transformed confidence relative to normal tissue, with lower peak sharpness, leftward displacement, and broader spread. Quantified with Welch's two-sample t-tests, sessile serrated lesions and hyperplastic polyps together produce a pronounced reduction in gland confidence versus normal tissue (t = -6.98, adjusted p < 0.001), tubular and tubulovillous adenomas produce a more modest reduction (t = -1.99, adjusted p = 0.047), and the two disease groups differ significantly from each other (t = 3.88, adjusted p < 0.001). GradCAM visualizations show the model attending to sawtooth luminal borders and branching crypt outlines in serrated lesions but to atypical, pseudostratified nuclei in adenomas. The authors read these signatures as evidence that the healthy-only model detects biologically meaningful deviations from normal glandular architecture that mirror the two known colorectal carcinogenesis pathways, and they position the shift patterns as potential computational biomarkers for precursor lesion characterization.

Load-bearing premise

The analysis assumes the confidence-score shifts on diseased gland patches are caused by biologically meaningful morphological deviations from normal tissue, not by trivial out-of-distribution artifacts such as different staining, scanner resolution, or patch composition; this interpretation is needed because the model never saw diseased tissue during training, so some drop in confidence on any diseased input is expected by default.

Editorial extensions

If this is right

  • Confidence-score shift patterns on specific HTTs such as glands can serve as candidate image-based biomarkers for separating serrated lesions from conventional adenomas, a distinction where even expert pathologists show notable inter-observer variability.
  • A model trained entirely on healthy tissue can be used as a screening probe, reducing the need for large, expensively annotated diseased-tissue datasets in early biomarker studies.
  • The ADPv2 dataset supplies the community with a colon-specific resource of 20,004 patches, each carrying a hierarchical multilabel annotation with an average of 10 labels, for training and benchmarking tissue-type classifiers.
  • The two-stage Barlow Twins plus VMamba pipeline with the asymmetric loss reaches a mean average precision of 0.88 across 14 HTTs, indicating the architecture scales to gigapixel pathology without down-sampling.
  • The co-occurrence structure of the taxonomy reproduces known features of colon wall architecture, such as the tight coupling between glands and inflammatory infiltrates, suggesting the label hierarchy captures biologically meaningful tissue relationships.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the confidence-shift signature were validated against molecular data such as BRAF or KRAS mutation status and CpG island methylator phenotype, the same healthy-only probe could extend to molecular subtyping of colorectal lesions; the paper does not run that test.
  • The healthy-baseline-as-probe strategy should transfer to other organs and diseases: any tissue with a granular normal-state taxonomy could have its confidence-shift patterns mined for disease signatures without collecting large diseased cohorts.
  • The per-HTT histograms could be compressed into scalar shift features (location, sharpness, spread per tissue type), turning the qualitative description into a quantitative multi-tissue fingerprint that a downstream classifier could use for polyp subtyping; the paper stops at univariate t-tests.
  • The diseased and normal RoI patches in the distribution analysis are matched in resolution and patch size, so a multi-site replication study across stains and scanners is the natural next test of whether the separation survives acquisition noise.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper introduces ADPv2, a publicly released dataset of 20,004 image patches extracted from 461 healthy colon biopsy slides, annotated with a hierarchical taxonomy of 32 histological tissue types (HTTs) across three levels. The authors train a VMamba encoder with Barlow Twins self-supervised pretraining and asymmetric-loss fine-tuning on a pruned 14-label subset, reporting a multilabel mAP of 0.879. They then apply this healthy-tissue-only model to RoI patches from four colorectal polyp subtypes (HP, SSL, TA, TVA) and analyze shifts in the model's confidence for the glands (GD) label, reporting three Welch t-tests that are interpreted as confirming the two canonical pathways of colorectal cancer development, namely the serrated pathway and the classical adenoma-carcinoma sequence. The paper's claimed contributions are the dataset, the two-stage model pipeline, and the confidence-distribution-based biomarker analysis.

Significance. If the results hold, the dataset contribution is genuinely useful: a public, hierarchically annotated healthy-colon patch dataset with a 32-label taxonomy is scarce, the data are released on Zenodo, the hyperparameters are fully tabulated in Tables B.5-B.7, and the train/test split is performed at the slide level, which prevents the most common form of data leakage. The classification result (mAP 0.879) provides a usable community baseline, and the confidence-shift probe is an interesting design. However, the most striking claim, that the confidence shifts confirm the two pathological pathways of colorectal cancer, is currently not supported by the statistical treatment as reported because the t-tests ignore clustering and the analysis lacks domain-shift controls; the specific problems and the path to fixing them are detailed in the major comments.

major comments (3)
  1. [Section 5.2, Table 4, Table B.8] The three Welch t-tests in Table 4 treat individual patches as independent observations, but the data are nested within slides and RoIs. Table B.8 reports 26,258 patches from 217 TVA slides (about 121 patches per slide) and 6,510 patches from 201 SSL slides, with multiple patches per RoI annotation. Slide- and RoI-level factors (patient, staining batch, scanning session, tissue processing) very plausibly induce intra-cluster correlation, so the effective sample sizes are much smaller than the reported patch counts. Even with a modest intraclass correlation of 0.3, the design effect for TVA is about 37, reducing the effective TVA sample from 26,258 to roughly 700 patches, and the reported t-statistics are inflated in precision by approximately the square root of the design effect; for the TVA-dominated TT-vs-Normal contrast this factor is on the order of 6-9, so the marginal t = -1.99, padj = 0.047 would very likely cease to be significant, and the TT-vs-SH contrast (t = 3.88) is also at risk. The paper reports no ICCs and no mixed-effects or cluster-robust analysis, so, as reported, the Table 4 p-values do not provide valid quantitative support for the claim that the patterns confirm the two cancer pathways. This analysis should be re-run with slide- and RoI-level random effects (or cluster-robust standard errors), and the ICCs and effect sizes should be reported.
  2. [Sections 4.2 and 5.2] The interpretation of the confidence-score shifts as biologically meaningful morphology changes is not supported without a domain-shift baseline. Because the model was trained exclusively on healthy tissue, lower confidence on any diseased input is expected a priori, and differences between disease groups could reflect staining, scanning, tissue composition, or annotation-density differences rather than pathway biology. The disease slides in Table B.8 are all listed at 20x/0.4 mpp, but their staining and source institution are not stated, and the normal-side distribution appears to be computed on the study's own healthy slides, some of which the model encountered during pretraining on 115,413 unlabelled patches and during fine-tuning; this confounds disease status with slide familiarity. The authors should add controls, for example confidence on non-gland HTTs in the same RoI patches, normal mucosa adjacent to the lesions, a held-out healthy cohort from a different institution, or stain-normalized versions of the same slides. In the absence of such controls, the abstract's claim that the analysis 'confirms the two pathological pathways' overstates what the evidence supports; at most the data are consistent with such an interpretation.
  3. [Section 5.1, Table 3] The 'strong classification performance' claim should be qualified in light of the per-class metrics in Table 3. LC and RBC achieve TPR above 0.99 with FPR of 0.913 and 0.961 and TNR of 0.087 and 0.039, meaning these two classes are predicted positively in nearly all patches, so their high F1 scores (0.928 and 0.954) largely reflect an 82% class prevalence rather than discriminative ability. Several diagnostically relevant inflammatory classes (ES, MA, PC, LY) have F1 between 0.62 and 0.79. The paper should report per-class average precision (the components of the mAP), state the mAP after excluding or down-weighting the near-degenerate classes, and temper the statement that the model performs strongly on 'HTTs pathologists consider diagnostically relevant.'
minor comments (6)
  1. [Section 4.1.3, Table 3] The manuscript says the label set is reduced 'from 32 labels to 14 labels' after merging surface epithelium into glands, yet Table 3 reports per-class metrics for 15 HTTs, including SE and GD as separate rows, and Figure 9 shows separate SE and GD heatmaps; the authors should clarify the actual training label set and how the SE row was computed if SE labels were merged into GD.
  2. [Section 5.1.1] The sentence 'EF has a high FPR, low TNR, and low accuracy' contradicts the EF row of Table 3 (FPR 0.034, TNR 0.966, accuracy 0.950); the surrounding text also calls neutrophils the sibling of EF under POC, which is inconsistent with Table 2 where EF is placed in the Glands branch, and the cross-reference to 'section 4.1.2' for neutrophils appears to point to the wrong section.
  3. [Table 4] The t-tests are reported only as t-statistics and adjusted p-values; with thousands of patches per group, even trivially small distribution shifts will reach p < 0.001, so effect sizes (e.g., Cohen's d or a distribution-overlap measure) are needed to support the qualitative description of 'pronounced' versus 'modest' shifts.
  4. [Section 4.2] The epsilon in the logit transform is printed as '1-6', which should read 10^-6; please fix the typesetting and state the histogram binning and normalization used for Figure 11.
  5. [Section 3.3] The annotation process relies on a single annotator trained under pathologist supervision, with subsequent pathologist evaluation, but no inter-annotator agreement statistic is reported; for a dataset paper, a kappa or F1 agreement measure on a re-annotated subset would substantially strengthen confidence in the 32-label taxonomy.
  6. [Section 5.1] The claim of demonstrating the merit of the VMamba architecture and the two-stage SSL pipeline would be strengthened by at least one baseline comparison (e.g., ImageNet-initialized VMamba, a ResNet or ViT with the same pretraining, or BCE instead of ASL); as it stands the mAP is reported without any comparator.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the biomarker claim is empirical and the mild self-citations are not load-bearing.

full rationale

No load-bearing circular step was found. The dataset and model claims are self-contained: the VMamba model is fine-tuned on slide-disjoint 80/10/10 splits and its mAP of 0.879 is measured on a held-out test split (Section 4.1.3, Table 3), so the classification result is not an artifact of the training objective. The confidence-score biomarker analysis (Sections 4.2 and 5.2) applies the frozen model to RoI patches from diseased slides that were never used in training (Table B.8); the t-tests compare model outputs on those new patches, and no parameter is fitted to the diseased data. The SH-vs-TT contrast is therefore an empirical observation, not a prediction forced by construction. The only self-citations (ADPv1 [18]; transferability of histological labels [52]) are background motivation and do not supply the statistical result; removing them would not alter any equation or p-value. The paper itself limits the claim in Section 6 ('could signal underlying structural abnormalities') and in Figure 12 ('pending further validation'). The reviewer's pseudoreplication concern about Table 4 (per-patch t-tests ignoring slide/RoI clustering; e.g., 26,258 patches from 217 TVA slides) is a statistical-validity issue, not a circularity, and does not change this verdict.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The dataset and model claims rest on annotation quality and taxonomy validity. The biomarker claim additionally rests on the assumption that confidence shifts reflect biology rather than domain shift. Hand-selected hyperparameters are the main free parameters.

free parameters (4)
  • Barlow Twins lambda = 0.0051
    Weight on off-diagonal terms in the Barlow Twins loss (Table B.5); chosen by hand, affects the pretraining representation.
  • ASL gamma positive = 1
    Asymmetric loss focusing parameter for positive samples (Table B.7); chosen by hand.
  • ASL gamma negative = 2
    Asymmetric loss focusing parameter for negative samples (Table B.7); chosen by hand.
  • ASL negative margin m = 0.05
    Clip parameter in the asymmetric loss for easy negatives (Table B.7); used in Equation 3 to shift negative probabilities, chosen by hand.
assumptions (5)
  • domain assumption The 32-label taxonomy is a valid, medically meaningful description of colon tissue.
    Defined by the authors' pathologist collaborators in Table 2; not benchmarked against another standard taxonomy.
  • domain assumption The single annotator's labels are accurate after expert pathologist review.
    Section 3.3 states one annotator was trained and all patches were reviewed; no inter-annotator agreement statistics are reported.
  • domain assumption RoI annotations for healthy and diseased slides are comparable.
    Section 4.2 uses pathologist-defined RoIs; healthy RoIs are areas where disease is likely to develop, while diseased RoIs are established lesions, so the two sets may differ in more than disease status.
  • domain assumption Confidence-score shifts are caused by tissue morphology rather than staining or scanner differences.
    The model is trained only on healthy tissue and tested on diseased slides with different patch counts and potentially different staining contexts (Table B.8); no stain normalization is applied for analysis.
  • standard math Logit-transformed confidence scores satisfy the assumptions of Welch's t-test.
    Section 4.2 applies a logit transform and uses Welch's two-sample t-test; this is a standard approximation given large sample sizes.

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Cite this review

Pith. "Pith review of ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease." pith.science (2026). https://pith.science/paper/EDHWR4JE

@misc{pith2026250705656,
  author       = {Pith},
  title        = {Pith review of: ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EDHWR4JE}},
  note         = {Machine review of arXiv:2507.05656}
}
read the original abstract

Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CoPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to multiple organs, but limit the capability for in-depth studies on specific organ diseases. Building upon this foundation, we introduce ADPv2, a novel dataset focused on gastrointestinal histopathology. Our dataset comprises 20,004 image patches derived from healthy colon biopsy slides, annotated according to a hierarchical taxonomy of 32 distinct HTTs of 3 levels. Furthermore, we train a multilabel representation learning model following a two-stage training procedure on our ADPv2 dataset. We leverage the VMamba architecture and achieving a mean average precision (mAP) of 0.88 in multilabel classification of colon HTTs. Finally, we show that our dataset is capable of an organ-specific in-depth study for potential biomarker discovery by analyzing the model's prediction behavior on tissues affected by different colon diseases, which reveals statistical patterns that confirm the two pathological pathways of colon cancer development. Our dataset is publicly available at https://zenodo.org/records/15307021

Figures

Figures reproduced from arXiv: 2507.05656 by the authors.

Figure 1
Figure 1. High-level overview of our WSI curation workflow. First, biopsy slides are [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Distribution of the number of HTTs found per annotated image patch in the [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Co-occurrence network for each level of HTTs in the taxonomy. The varying [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Example patches annotated with arrows indicating the regions containing HTTs. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Representative examples of Level 3 HTTs. Each panel displays a cropped region [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Training pipeline of the VMamba model. First, WSIs are split into fixed size 544 [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: RoI Confidence Score Distribution Analysis Workflow. Pathologists annotate [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: Visualization of image embeddings using t-SNE. Each HTT is denoted by a [PITH_FULL_IMAGE:figures/full_fig_p023_8.png]
Figure 9
Figure 9. Figure 9: The heatmaps for diagnostically relevant HTTs on a healthy slide and a tubulovil [PITH_FULL_IMAGE:figures/full_fig_p024_9.png]
Figure 10
Figure 10. Figure 10: GradCAM vs. Original images across 14 HTTs from ADPv2. Rows 1 and 3 [PITH_FULL_IMAGE:figures/full_fig_p025_10.png]
Figure 10
Figure 10. Figure 10: Whereas for the majority of HTTs, activation maps as seen with Grad￾CAM have mostly focused directly on the HTT of interest (e.g., LA, lym￾phocytes; GD, glands; SMC, smooth muscle cells), in other instances, salient features most used by the model as visualized by the…
Figure 11
Figure 11. Figure 11: Predicted confidence score histogram for glands. (a) The histogram for healthy [PITH_FULL_IMAGE:figures/full_fig_p026_11.png]
Figure 12
Figure 12. Figure 12: Sampled gland patches from each disease. The model is focused on the shape [PITH_FULL_IMAGE:figures/full_fig_p028_12.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.