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arxiv: 2605.05522 · v1 · submitted 2026-05-06 · 📡 eess.IV · cs.CV

Recognition: unknown

Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images

Aneesh Rangnekar , Joao Miranda , Natally Horvat , Stephanie Chahwan , Samir Alrayess , Aditya Apte , Aditi Iyer , Eve LoCastro
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Revathi Ravella Marc J Gollub Iva Petkovska Jesse Joshua Smith Paul Romesser Julio Garcia-Aguilar Harini Veeraraghavan Joseph Deasy
Authors on Pith no claims yet

Pith reviewed 2026-05-08 15:17 UTC · model grok-4.3

classification 📡 eess.IV cs.CV
keywords rectal cancer segmentationCT-to-MRI transfertransformer pretrainingattention dilutionmedical image augmentationhierarchical transformers
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The pith

Tumor-aware augmentation and anisotropic cropping restore token efficiency in CT-pretrained transformers for rectal MRI segmentation.

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

The paper demonstrates that common assumptions about dataset adaptation and cross-modality transfer break down when applying CT-pretrained hierarchical transformers to rectal cancer segmentation in MRI scans. Two specific failure modes appear: zero-padding creates inefficient token usage by diverting attention to uninformative regions, and pretrained features show limited adaptation despite fine-tuning. Using an attention dilution index to quantify padding effects and centered kernel alignment to track feature reuse, the authors test two interventions on SMIT and Swin UNETR backbones. Tumor-aware augmentation increases coverage of tumor appearance variations while anisotropic cropping eliminates wasteful padding. These changes raise detection rates to 224 out of 247 cases for one model and 219 out of 247 for the other on the same rectal MRI test set.

Core claim

Mechanistic analysis of attention dilution and feature reuse shows that zero-padding and ineffective adaptation cause accuracy loss in CT-to-MRI transfer; tumor-aware augmentation plus anisotropic cropping directly mitigate both issues and improve detection rates to 90.7 percent and 88.7 percent on identical rectal MRI datasets for SMIT and Swin UNETR respectively.

What carries the argument

Attention dilution index (ADI), an entropy-based metric that measures how much attention is diverted to zero-padded tokens, used together with centered kernel alignment (CKA) to assess feature reuse across modalities.

Load-bearing premise

The failure modes of token inefficiency and poor feature adaptation are the main reasons for degraded transfer performance and can be fixed by the proposed augmentation and cropping steps without creating selection bias.

What would settle it

Applying the same tumor-aware augmentation and anisotropic cropping to a model trained from scratch on MRI data alone produces no detection-rate improvement over standard fine-tuning.

Figures

Figures reproduced from arXiv: 2605.05522 by Aditi Iyer, Aditya Apte, Aneesh Rangnekar, Eve LoCastro, Harini Veeraraghavan, Iva Petkovska, Jesse Joshua Smith, Joao Miranda, Joseph Deasy, Julio Garcia-Aguilar, Marc J Gollub, Natally Horvat, Paul Romesser, Revathi Ravella, Samir Alrayess, Stephanie Chahwan.

Figure 1
Figure 1. Figure 1: Tumor-aware intensity augmentation applied to representative training cases. Each row shows an axial slice with manual delineation (green), followed by three augmented variants in which tumor-region intensities are randomly scaled and shifted. perturbations that broadened tumor appearance without in￾troducing implausible contrast inversions or overwhelming the underlying anatomy. The perturbation was appli… view at source ↗
Figure 2
Figure 2. Figure 2: Rows A–C show representative high-contrast tumors (Cluster-B), while rows D–F show low-contrast tumors (Cluster-D). Each row presents the MRI slice with manual delineation (green), followed by predictions from SMIT and Swin UNETR across the three configurations. Surface DSC (sDSC) is shown in the lower-right corner of each prediction panel. Missing values indicate that the tumor was not detected. The final… view at source ↗
Figure 3
Figure 3. Figure 3: Representative challenging tumors under the ACT configuration. Each row shows an axial MRI slice with reference contour (green), followed by predictions from SMIT-ACT and Swin UNETR-ACT (orange). Surface DSC (sDSC) is shown in the prediction panels; ‘Not detected’ indicates failed tumor detection. to the pretrained representations across most layers, indi￾cating that tumor-aware augmentation primarily infl… view at source ↗
Figure 4
Figure 4. Figure 4: Attention dilution index (ADI) analysis linking hierarchical depth, padding fraction, and segmentation accuracy. (A–B) Mean ADI per stage for SMIT and Swin UNETR; the shaded region highlights the S0–S2 window artifact in Swin UNETR. (C) Mean Stage-3 ADI illustrating the effect of anisotropic cropping across both backbones. (D) Per-scan Stage-3 ADI as a function of padding fraction (𝑝𝑓), with per-backbone S… view at source ↗
Figure 5
Figure 5. Figure 5: Two tumor subtypes identified by unsupervised clus￾tering were consistent across cohorts. Tumor-to-background contrast versus boundary gradient for the training (A) and held-out test set (B). Cluster-B (blue) tumors show higher contrast and sharper boundaries than Cluster-D (orange). Donut insets show subtype proportions. Note that one contrast outlier was excluded from panel B. Through mechanistic analysi… view at source ↗
Figure 6
Figure 6. Figure 6: Layer-wise feature similarity to the pretrained backbone measured using linear CKA. Diagonal CKA across transformer blocks for (a) SMIT and (b) Swin UNETR under different configurations. 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Training loss a 0.50 0.55 0.60 0.65 Validation Dice b Epoch 0.25 0.50 0.75 1.00 1.25 1.50 Training loss c Epoch 0.500 0.525 0.550 0.575 0.600 0.625 0.650 Validation Dice d Base Tumor-awar… view at source ↗
Figure 8
Figure 8. Figure 8: Layer-wise feature similarity between randomly initialized (Scratch) and CT-pretrained (CT-Init.) models mea￾sured using diagonal linear CKA across transformer blocks for SMIT and Swin UNETR. evaluation was performed using retrospectively collected data from a single institution and primarily GE Health￾care scanners, limiting assessment of robustness across vendors, field strengths, acquisition protocols, … view at source ↗
read the original abstract

Pretraining on large-scale datasets has been shown to improve transformer generalizability, even for out-of-domain (OOD) modalities and tasks. However, two common assumptions often fail under OOD transfer: that downstream datasets can be adapted to the fixed input geometry of pretrained models and that pretrained representations transfer effectively across imaging modalities. We show that these assumptions break down through two interacting failure modes in CT-to-MRI transfer: inefficient token usage caused by zero-padding to match pretrained input dimensions and ineffective feature adaptation. These failures led to accuracy degradation despite extensive fine-tuning. We investigated these failure modes using two CT-pretrained hierarchical shifted-window transformer backbones, SMIT and Swin UNETR, pretrained with different objectives and datasets. Mechanistic analysis introduced an attention dilution index (ADI), an entropy-based metric quantifying attention diverted toward uninformative padding tokens, and centered kernel alignment (CKA) to measure feature reuse in MRI tasks. ADI increased with zero-padding, while high feature reuse did not necessarily correspond to improved accuracy. To mitigate these issues, we introduced two interventions: a tumor-aware augmentation strategy to improve tumor appearance heterogeneity coverage and an anisotropic cropping strategy to restore token efficiency. Fine-tuning on identical rectal MRI datasets improved detection rates to 224/247 (90.7%) for SMIT and 219/247 (88.7%) for Swin UNETR, demonstrating improved robustness under CT-to-MRI transfer. This study is among the first to examine when pretrained transformers fail to transfer effectively across imaging modalities and how simple mitigation strategies, motivated by mechanistic analysis of datasets, can reduce transfer limitations while improving robustness and MRI detection.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper examines failure modes in transferring CT-pretrained hierarchical shifted-window transformers (SMIT and Swin UNETR) to rectal MRI segmentation tasks. It identifies two interacting issues—inefficient token usage from zero-padding to match pretrained input sizes and ineffective feature adaptation across modalities—using a new Attention Dilution Index (ADI) based on entropy and centered kernel alignment (CKA) for mechanistic analysis. The authors propose tumor-aware augmentation to increase tumor heterogeneity coverage and anisotropic cropping to restore token efficiency, reporting post-intervention detection rates of 224/247 (90.7%) for SMIT and 219/247 (88.7%) for Swin UNETR after fine-tuning on rectal MRI data, claiming improved robustness under CT-to-MRI transfer.

Significance. If the central improvements can be causally attributed to the proposed interventions via proper controls, the work would offer useful insights into cross-modality transfer limitations for medical vision transformers and practical, low-cost mitigation strategies. This could have moderate significance for improving segmentation robustness in rectal cancer MRI, where pretrained models are increasingly used but often degrade on OOD data.

major comments (3)
  1. [Abstract and Results] Abstract and Results: The final detection rates of 224/247 (90.7%) for SMIT and 219/247 (88.7%) for Swin UNETR are presented as evidence of improved robustness, but the corresponding rates from the prior 'extensive fine-tuning' (which the text states exhibited degradation) are not reported, preventing quantification of the actual improvement magnitude attributable to the interventions.
  2. [Methods/Results] Experimental design (implied in Methods/Results): No ablation studies are provided that apply tumor-aware augmentation alone, anisotropic cropping alone, or neither intervention (beyond the baseline extensive fine-tuning), which is load-bearing because the central claim attributes the accuracy gains specifically to these two strategies mitigating token inefficiency and feature adaptation failures.
  3. [Analysis] Analysis section: ADI and CKA are introduced and described as increasing with zero-padding and measuring feature reuse, respectively, but the manuscript does not include quantitative correlation (e.g., regression or per-case analysis) between ADI/CKA values and the final accuracy deltas, weakening the mechanistic justification for the mitigations.
minor comments (2)
  1. [Abstract] The abstract uses 'detection rates' for what is described as a segmentation task; clarify the exact metric (e.g., whether it is tumor presence detection within segmented volumes or a proxy for segmentation performance) to avoid ambiguity.
  2. [Methods] The definition and computation of the Attention Dilution Index (ADI) should be formalized with an equation in the main text rather than described only qualitatively, to allow reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped us identify areas where the manuscript can be strengthened. We address each major comment below and have revised the manuscript accordingly to provide clearer quantification of improvements, additional controls, and stronger mechanistic evidence.

read point-by-point responses

  1. Referee: [Abstract and Results] Abstract and Results: The final detection rates of 224/247 (90.7%) for SMIT and 219/247 (88.7%) for Swin UNETR are presented as evidence of improved robustness, but the corresponding rates from the prior 'extensive fine-tuning' (which the text states exhibited degradation) are not reported, preventing quantification of the actual improvement magnitude attributable to the interventions.

    Authors: We agree that explicit baseline rates are needed for direct comparison. The manuscript text notes degradation under extensive fine-tuning alone, and the underlying per-model detection rates from that condition are available from our experiments. In the revised version, we will report these baseline rates alongside the post-intervention figures in both the abstract and results sections to quantify the improvement magnitude. revision: yes

  2. Referee: [Methods/Results] Experimental design (implied in Methods/Results): No ablation studies are provided that apply tumor-aware augmentation alone, anisotropic cropping alone, or neither intervention (beyond the baseline extensive fine-tuning), which is load-bearing because the central claim attributes the accuracy gains specifically to these two strategies mitigating token inefficiency and feature adaptation failures.

    Authors: We acknowledge that separate ablations would strengthen attribution of gains to each intervention. Our original design emphasized the combined application motivated by the interacting failure modes identified via ADI and CKA. In the revision, we will add ablation experiments applying tumor-aware augmentation alone and anisotropic cropping alone, reporting their individual effects on detection rates, ADI, and CKA to isolate contributions. revision: yes

  3. Referee: [Analysis] Analysis section: ADI and CKA are introduced and described as increasing with zero-padding and measuring feature reuse, respectively, but the manuscript does not include quantitative correlation (e.g., regression or per-case analysis) between ADI/CKA values and the final accuracy deltas, weakening the mechanistic justification for the mitigations.

    Authors: We appreciate this point on strengthening the mechanistic link. The current analysis shows ADI rising with padding and CKA patterns for feature reuse, but lacks explicit correlation to accuracy. In the revised manuscript, we will add per-case scatter plots, Pearson/Spearman correlations, and regression analysis between ADI/CKA values and segmentation accuracy deltas across cases to provide quantitative support for the proposed mitigations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical study with independent metrics and reported rates

full rationale

The paper is an empirical transfer-learning study. It defines ADI (entropy-based attention metric) and CKA externally, applies tumor-aware augmentation and anisotropic cropping as interventions, and reports concrete detection rates (224/247, 219/247) on rectal MRI data. No equations, fitted parameters, or predictions are shown to reduce by construction to the inputs; the central claim rests on experimental outcomes and mechanistic analysis rather than self-referential definitions or self-citation chains. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that the proposed interventions address the diagnosed transfer failures and that the reported detection rates reflect genuine robustness gains rather than dataset-specific effects.

axioms (1)
  • domain assumption Pretrained hierarchical shifted-window transformers on CT data can be meaningfully fine-tuned for MRI segmentation tasks
    Invoked throughout the transfer learning setup described in the abstract.
invented entities (1)
  • Attention Dilution Index (ADI) no independent evidence
    purpose: Entropy-based metric to quantify attention diverted to uninformative padding tokens
    Newly introduced diagnostic tool for analyzing transformer behavior under zero-padding.

pith-pipeline@v0.9.0 · 5673 in / 1327 out tokens · 41153 ms · 2026-05-08T15:17:18.738165+00:00 · methodology

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