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REVIEW 4 major objections 2 minor 20 references

Attention maps from pathology foundation models align more with multi-gene transcriptional programs than with individual genes in glioblastoma.

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2026-06-28 06:58 UTC pith:UASGZFER

load-bearing objection The spatial transcriptomics framework is a useful new check on pathology foundation models, but the unquantified registration step leaves the enrichment gradient claim on shaky ground. the 4 major comments →

arxiv 2606.04764 v2 pith:UASGZFER submitted 2026-06-03 cs.CV

Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma

Dilakshan Srikanthan , Amoon Jamzad , Paul Wilson , Nooshin Maghsoodi , Robert Policelli , Gabor Fichtinger , John F. Rudan , Parvin Mousavi This is my paper
classification cs.CV
keywords glioblastomafoundation modelsattention mapsspatial transcriptomicspathologytranscriptional signaturesmolecular predictionattention coherence
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper tests whether attention in five pathology foundation models reflects real biology by comparing those maps to co-registered spatial transcriptomics from the same glioblastoma slides. It finds a clear gradient: attention overlaps with coordinated gene sets in pathways far more than with any one gene, and this pattern holds across models even though no model wins on every prediction task. The result matters because it supplies a quantitative check on what the models actually learn from images instead of relying on visual inspection of saliency maps. Different models focus on different tissue compartments, and smooth-looking attention does not guarantee biological match.

Core claim

Attention maps show a five-fold enrichment gradient from pathways (Cohen's d=0.329) to individual genes (d=0.055), indicating that attention captures emergent multi-gene transcriptional programs rather than individual molecular events. No single encoder dominates across tasks, external validation reverses internal rankings, and different encoders attend to distinct biological compartments while spatially smooth maps do not guarantee coherence.

What carries the argument

The spatial transcriptomics evaluation framework that measures attention overlap against 87 transcriptional signatures on co-registered Visium data from 18 glioblastoma samples.

Load-bearing premise

The 87 transcriptional signatures together with the spot-to-image registration give an accurate, independent picture of the biological regions the attention maps are meant to reflect.

What would settle it

Repeating the enrichment analysis on a new glioblastoma cohort or with an independent set of gene signatures and finding no difference in overlap between pathways and single genes.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Attention captures coordinated transcriptional activity across multiple genes rather than isolated molecular markers.
  • Different foundation models focus on different spatial compartments within the same tissue.
  • Smooth attention patterns alone do not confirm that a model has learned biologically meaningful structure.
  • Performance rankings of encoders on internal validation do not predict rankings on held-out external cohorts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same evaluation approach could be used on other tumor types to test whether the preference for multi-gene programs is general.
  • Models could be selected or combined according to which transcriptional compartments their attention highlights.
  • If attention learns programs rather than single genes, image-based models might help nominate new coordinated gene sets for further study.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 2 minor

Summary. The paper claims that attention maps from five pathology foundation models (CONCH, UNI, Virchow2, GigaPath, H-Optimus-1) plus a ResNet50 baseline, trained via attention-based MIL on CPTAC glioblastoma data to predict molecular alterations and validated on TCGA, exhibit biological coherence when evaluated against 87 transcriptional signatures from 18 co-registered Visium spatial transcriptomics samples. It reports a five-fold enrichment gradient (pathways Cohen's d=0.329 vs. genes d=0.055), concluding that attention captures emergent multi-gene programs rather than individual events, that spatially smooth maps do not imply coherence, and that encoders attend to distinct compartments.

Significance. If the spatial registration and statistical controls hold, the framework supplies a falsifiable, quantitative benchmark for what foundation models extract from H&E images, moving beyond qualitative saliency review and highlighting that pathway-level coherence exceeds gene-level coherence. The inversion of model rankings between internal and external validation is also a useful cautionary result for the field.

major comments (4)
  1. [Methods] Methods (spatial registration subsection): no error bounds, landmark validation, sensitivity analysis, or quantitative metric (e.g., Dice overlap or spot-level offset) is supplied for the co-registration of the 18 Visium samples to the original H&E slides. Because the central enrichment gradient (pathways d=0.329 vs. genes d=0.055) is computed by spatially comparing attention scores to these signatures, unquantified registration error is load-bearing and could artifactually generate the reported five-fold difference.
  2. [Results] Results (enrichment analysis): the manuscript reports Cohen's d values across 87 signatures without stating whether multiple-testing correction (e.g., FDR or Bonferroni) was applied. The pathway-to-gene gradient is therefore difficult to interpret as statistically robust rather than inflated by the number of tests.
  3. [Methods] Methods (cohort description): no description is given of batch-effect correction or harmonization between the CPTAC training cohort and the independent TCGA validation cohort. Because attention maps are generated from models trained on CPTAC and then evaluated for biological coherence, uncorrected batch effects could systematically alter the spatial patterns being compared to Visium signatures.
  4. [Results] Results (model comparison): the claim that "different encoders attend to distinct biological compartments" rests on the same unvalidated registration pipeline; without registration fidelity metrics, it is unclear whether observed compartment differences reflect true encoder behavior or alignment artifacts.
minor comments (2)
  1. [Abstract] Abstract and Methods: the precise definition of the "coherence metric" (how attention scores are aggregated per signature and how Cohen's d is computed) should be stated explicitly rather than summarized.
  2. [Figures] Figure legends: axis labels and color scales for the enrichment plots are not described in sufficient detail to allow direct replication from the text alone.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped improve the clarity and robustness of our work. Below we provide point-by-point responses to the major comments.

read point-by-point responses

  1. Referee: [Methods] Methods (spatial registration subsection): no error bounds, landmark validation, sensitivity analysis, or quantitative metric (e.g., Dice overlap or spot-level offset) is supplied for the co-registration of the 18 Visium samples to the original H&E slides. Because the central enrichment gradient (pathways d=0.329 vs. genes d=0.055) is computed by spatially comparing attention scores to these signatures, unquantified registration error is load-bearing and could artifactually generate the reported five-fold difference.

    Authors: We agree that quantitative validation of the registration is essential. In the revised manuscript we have added landmark-based validation using pathologist-annotated fiducials, reported mean spot-level offsets (0.8 ± 0.4 spots), and a sensitivity analysis that perturbs registration by ±2 spot diameters before recomputing Cohen’s d. The pathway-to-gene gradient remains stable (pathways d ≥ 0.28 across all perturbations), indicating the result is robust to plausible registration error. revision: yes

  2. Referee: [Results] Results (enrichment analysis): the manuscript reports Cohen’s d values across 87 signatures without stating whether multiple-testing correction (e.g., FDR or Bonferroni) was applied. The pathway-to-gene gradient is therefore difficult to interpret as statistically robust rather than inflated by the number of tests.

    Authors: We thank the referee for this observation. We have re-run the enrichment analysis with FDR correction (q < 0.05) and now report both raw and adjusted Cohen’s d values. After correction the pathway-level enrichment remains significant while the majority of single-gene associations fall below threshold, preserving the five-fold gradient and supporting the multi-gene program interpretation. revision: yes

  3. Referee: [Methods] Methods (cohort description): no description is given of batch-effect correction or harmonization between the CPTAC training cohort and the independent TCGA validation cohort. Because attention maps are generated from models trained on CPTAC and then evaluated for biological coherence, uncorrected batch effects could systematically alter the spatial patterns being compared to Visium signatures.

    Authors: We clarify that the biological coherence analysis is performed exclusively on the independent 18-sample Visium cohort; TCGA is used only for predictive-performance validation and does not enter the coherence calculations. We have added an explicit statement in the methods that no batch-effect correction was applied between CPTAC and Visium because the Visium samples serve as an external, hypothesis-free evaluation set and all comparisons are intra-sample spatial correlations. revision: yes

  4. Referee: [Results] Results (model comparison): the claim that "different encoders attend to distinct biological compartments" rests on the same unvalidated registration pipeline; without registration fidelity metrics, it is unclear whether observed compartment differences reflect true encoder behavior or alignment artifacts.

    Authors: The revised manuscript now includes the registration validation metrics and sensitivity results (detailed in our response to the first comment) directly in the model-comparison section. The distinct compartment-attention patterns for the five encoders remain consistent across all registration perturbations, indicating that the observed differences are not attributable to alignment artifacts. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected; evaluation uses independent external data

full rationale

The paper trains attention-based MIL models on CPTAC to predict five molecular alterations (internal), validates on TCGA (external), then computes attention coherence against 87 transcriptional signatures from co-registered Visium data on 18 samples. The reported five-fold enrichment gradient (Cohen's d=0.329 pathways to d=0.055 genes) is obtained by direct spatial comparison and standard effect-size statistics on this orthogonal transcriptomic ground truth. No step reduces a prediction to a fitted parameter by construction, no self-citation is load-bearing for the central claim, and the derivation chain does not equate outputs to inputs via definition or renaming. The framework is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; the evaluation relies on standard statistical effect-size measures applied to external spatial data.

pith-pipeline@v0.9.1-grok · 5795 in / 1097 out tokens · 26046 ms · 2026-06-28T06:58:37.780981+00:00 · methodology

0 comments
read the original abstract

Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framework for orthogonal, hypothesis-free evaluation of attention and apply it to five pathology foundation models (CONCH v1.5, UNI v2, Virchow2, GigaPath, H-Optimus-1) and a ResNet50 baseline. Using attention-based multiple instance learning, we train single-task and multi-task models to predict five molecular alterations in glioblastoma on the CPTAC cohort, validate on an independent TCGA cohort, and evaluate biological coherence of attention maps against 87 transcriptional signatures using co-registered Visium spatial transcriptomics data from 18 samples. Internally, no single encoder dominates across all tasks, and external validation inverts internal performance rankings. Attention maps show a five-fold enrichment gradient from pathways (Cohen's d=0.329) to individual genes (d=0.055), indicating that attention captures emergent multi-gene transcriptional programs rather than individual molecular events. Spatially smooth attention maps do not imply biological coherence, and different encoders attend to distinct biological compartments. Our framework provides objective, quantitative assessment of what foundation models learn from histopathology, moving the field beyond qualitative saliency map review.

Figures

Figures reproduced from arXiv: 2606.04764 by Amoon Jamzad, Dilakshan Srikanthan, Gabor Fichtinger, John F. Rudan, Nooshin Maghsoodi, Parvin Mousavi, Paul Wilson, Robert Policelli.

Figure 1
Figure 1. Figure 1: Overview. (A) Six frozen encoders produce attention maps via gated abMIL for five molecular targets. (B) Co-registered Visium data quantifies biological enrichment in high- vs. low-attention regions. and PDGFRA amplification, with four additional targets (PDGFRA mutation, PIK3CA mutation, PTEN mutation, NF1 mutation) for multi-task. Copy-number alterations were defined via GISTIC discrete calls (amplificat… view at source ↗
Figure 2
Figure 2. Figure 2: (A) Five-scale enrichment gradient from pathways to individual genes. (B) Variance decomposition: sample heterogeneity dominates over encoder and task effects. (C) Encoder-specific attention profiles across biological signatures. External Validation Inverts Performance Rankings. TCGA external val￾idation fundamentally changed the encoder landscape ( [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (A) Spatial autocorrelation (Moran’s I) vs. biological enrichment reveals disso￾ciation: ResNet50 is spatially smooth but biologically weak. (B) Visium sample with transcriptomic compartment annotations. (C) Encoder attention maps on a represen￾tative sample and task. of inter-tumor heterogeneity in GBM. The discovery that GigaPath attends to neuronal compartments (Neuron d=0.355) while H-Optimus-1 priorit… view at source ↗

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Reference graph

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