arxiv: 2605.10054 · v1 · submitted 2026-05-11 · 💻 cs.CV

Recognition: 1 theorem link

· Lean Theorem

Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging

Zubair Faruqui , Rahul Dubey

Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:11 UTC · model grok-4.3

classification 💻 cs.CV

keywords explanation-aware learninginterpretabilitybiomedical imagingsaliency mapsexplanation losschest X-raydeep neural networksannotation coverage

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The pith

Incorporating explanation loss during training improves alignment of medical image model explanations with clinical annotations while keeping accuracy comparable.

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

Deep neural networks for biomedical image diagnosis frequently rely on spurious visual cues that undermine trust in their outputs. The paper integrates explanation supervision directly into the training objective to steer model attention toward regions marked in clinical annotations. Different designs and strengths of this added loss are tested for effects on both accuracy and the spatial faithfulness of resulting explanations. Two new quantitative metrics, annotation coverage and saliency precision, enable objective measurement beyond visual inspection. Experiments on annotated chest X-ray data show that suitable explanation loss coefficients produce consistently better alignment without meaningful accuracy loss, with guidance offered for handling noisy annotations across imaging modalities.

Core claim

The paper claims that adding an explanation loss term to the training objective of deep neural networks for biomedical images guides attention to clinically meaningful regions, yielding quantitatively improved explanation alignment as measured by annotation coverage and saliency precision while maintaining comparable predictive accuracy on chest X-ray datasets.

What carries the argument

Explanation loss added to the training objective, measured by the new metrics of annotation coverage and saliency precision, to enforce spatial faithfulness to clinical annotations.

If this is right

A clear trade-off exists between explanation quality and the coefficient strength of the added loss term.
Quantitative statistical analysis shows consistently improved explanation alignment across tested configurations.
The approach extends to a broad range of annotated biomedical imaging modalities beyond chest X-rays.
Practical guidance emerges for incorporating explanation loss under conditions of noisy clinical annotations.

Where Pith is reading between the lines

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

This method could reduce reliance on separate post-hoc explanation techniques when deploying medical imaging models.
High-quality clinical annotations become even more critical for effective model guidance during training.
The framework might be tested on modalities such as MRI or CT to check for similar gains in explanation faithfulness.
It could increase model resistance to learning from confounding or spurious image features.

Load-bearing premise

The provided clinical annotations reliably identify the features the model should use, and balancing the explanation loss does not introduce new unintended biases.

What would settle it

Training identical models with and without the explanation loss on a held-out set of annotated images and finding no improvement in annotation coverage or saliency precision would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.10054 by Rahul Dubey, Zubair Faruqui.

**Figure 1.** Figure 1: Construction of the annotation mask M from expert-provided bounding boxes. (a) Multiple coarse bounding box annotations provided by radiologists for a disease-positive chest X-ray. Individual boxes may cover large regions containing both pathological and normal tissue. (b) Binary union mask M (in red) formed by merging all bounding boxes into a single spatial supervision signal aligned with the GradCAM re… view at source ↗

**Figure 2.** Figure 2: (a) Comparison of training dynamics between explanation-guided and standard BCE-trained models for the pleural effusion disease classification task (α = 0.25), using the Logit Difference Square formulation. The guided model minimizes a weighted sum of its BCE loss and explanation penalty terms, while the standard model optimizes BCE loss alone. The close alignment between the guided model’s total loss and … view at source ↗

**Figure 3.** Figure 3: Qualitative comparison of Grad-CAM explanations for pleural effusion. (a): standard training produces diffuse saliency spanning large regions of the image. (b): the proposed explanation-guided model yields focused localization aligned with expert-annotated disease regions (white boxes). standard BCE training (a) and the proposed explanationguided approach (b), respectively. When trained with BCE alone, th… view at source ↗

**Figure 4.** Figure 4: Effect of explanation loss coefficient on predictive performance (a) and explanation quality across seven disease categories (b). In fig (a), box plots summarize distributions of test accuracies obtained over different values of α for seven loss formulations and for seven diseases, a total of 49 test accuracy values. As α increases, the mean accuracy decreases, but median remains stable. Fig (b) shows aver… view at source ↗

**Figure 5.** Figure 5: Effect of explanation loss formulation on predictive performance and explanation quality. Loss types are denoted using compact abbreviations on the X-axis. LD and P denote logit-difference and probability-difference explanation losses, respectively; Abs, Algeb, and Sqr indicate absolute, algebraic, and squared variants. L-Only applies explanation supervision directly to the target class logit without pairw… view at source ↗

read the original abstract

Deep neural networks for medical image diagnosis often achieve high predictive accuracy while relying on spurious or clinically irrelevant visual cues, limiting their trustworthiness in practice. Post-hoc explanation methods are widely used to visualize model decisions in the form of saliency maps; however, these explanations do not influence how models learn during training, allowing non-causal or confounding features to persist. This motivates the incorporation of explanation supervision directly into the training objective to guide model attention toward clinically meaningful regions and promote clinically grounded decision-making. This paper presents a systematic approach to integrate explanation loss into model training and analyzes how different explanation loss designs and supervision strengths influence both predictive performance and spatial faithfulness of explanations. To quantitatively assess interpretability, two complementary explanation performance metrics-annotation coverage and saliency precision-are introduced, enabling rigorous evaluation beyond qualitative visualization. Our experimental results reveal a clear trade-off between explanation quality and explanation loss coefficients. Furthermore, quantitative statistical analysis yields consistently improved explanation alignment while maintaining comparable accuracy. Experiments were conducted on annotated chest X-ray datasets; however, the proposed framework is applicable to a broad range of annotated biomedical imaging modalities. Overall, these findings demonstrate that explanation supervision is not a monolithic design choice and provide practical guidance for incorporating explanation loss into training objectives under noisy clinical annotations.

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.

Desk Editor's Note private letter to a colleague

This abstract-only paper introduces two new metrics for explanation quality but gives no methods or numbers to check if the claimed improvements hold.

read the letter

The main thing to know is that the work adds annotation coverage and saliency precision as quantitative ways to score how well model explanations line up with clinical annotations, and it runs a systematic check on different explanation loss designs and their strengths during training. That moves past just qualitative saliency maps and tries to make the supervision choices more deliberate. It does a reasonable job framing the problem of models latching onto spurious cues in medical images and showing why baking explanation terms into the training objective could help steer attention to relevant regions. The note on trade-offs with accuracy and the broad applicability claim to other annotated imaging modalities are also sensible points if the experiments back them up. The soft spots are straightforward because only the abstract is here. There are no loss equations, no training details, no dataset sizes, no statistical tests, and no actual performance numbers or error bars to see. The claim of consistently improved alignment with maintained accuracy cannot be verified, and the paper itself flags noisy clinical annotations without saying how that noise was managed or whether it introduced new biases. This leaves the central results untestable from what is provided. The paper would mainly interest researchers working on explainable AI for biomedical imaging who are already thinking about attention supervision and want concrete metrics to try. A reader looking for practical advice on loss coefficients might find the systematic angle useful if the full experiments are solid. I would not cite it in its current form. The topic is worth referee time, so I would send the full paper out for review to see whether the methods and results actually support the claims.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes integrating explanation supervision directly into the training objective of deep neural networks for biomedical image diagnosis. It systematically analyzes the effects of different explanation loss designs and supervision strengths on both predictive accuracy and spatial faithfulness of explanations. New quantitative metrics (annotation coverage and saliency precision) are introduced to evaluate interpretability beyond qualitative saliency maps. Experiments on annotated chest X-ray datasets are reported to show a trade-off between explanation quality and loss coefficients, yet overall improved alignment with clinical annotations while maintaining comparable accuracy. The framework is presented as applicable to other annotated biomedical imaging modalities.

Significance. If the empirical claims hold, the work would supply practical guidance for training more trustworthy models in medical imaging by explicitly balancing accuracy against explanation alignment under noisy annotations. The introduction of complementary quantitative metrics for spatial faithfulness represents a constructive step beyond purely qualitative assessment of interpretability.

major comments (2)

[Abstract] Abstract: the central claim that 'quantitative statistical analysis yields consistently improved explanation alignment while maintaining comparable accuracy' is asserted without any equations, training details, statistical tests, error bars, or result tables, rendering the claim unverifiable from the provided text and load-bearing for the paper's contribution.
[Abstract] Abstract: the approach relies on the assumption that clinical annotations reliably indicate the features models should attend to, yet the text acknowledges 'noisy clinical annotations' without specifying how the explanation-loss balancing avoids introducing new unintended biases or how robustness to annotation noise was tested.

minor comments (1)

[Abstract] Abstract: the metrics 'annotation coverage' and 'saliency precision' are named but receive no operational definitions or formulas, which would aid immediate understanding even in a high-level summary.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments on our manuscript. We address each major comment point by point below, indicating whether revisions will be incorporated.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that 'quantitative statistical analysis yields consistently improved explanation alignment while maintaining comparable accuracy' is asserted without any equations, training details, statistical tests, error bars, or result tables, rendering the claim unverifiable from the provided text and load-bearing for the paper's contribution.

Authors: The abstract is intentionally concise and summarizes the primary findings. The full manuscript supplies all requested elements: the combined training objective and explanation loss terms are formalized in Equations (2) and (3) of Section 2; the complete training protocol, including optimizer settings and hyperparameter ranges, appears in Section 3.1; statistical significance is assessed via paired t-tests and Wilcoxon tests with reported p-values in Section 4.2; error bars are shown on all bar plots in Figures 3–5 and Tables 1–2; and full numerical results are tabulated in Section 4. Because the referee has access to the complete paper, the central claim is directly verifiable there. We will add one sentence to the abstract noting that improvements are statistically significant (p < 0.05). revision: partial
Referee: [Abstract] Abstract: the approach relies on the assumption that clinical annotations reliably indicate the features models should attend to, yet the text acknowledges 'noisy clinical annotations' without specifying how the explanation-loss balancing avoids introducing new unintended biases or how robustness to annotation noise was tested.

Authors: The abstract explicitly references guidance under noisy clinical annotations. The balancing mechanism is the scalar coefficient λ multiplying the explanation term; Section 4.1 reports a systematic sweep over λ values that identifies operating points where annotation alignment rises while classification accuracy remains statistically indistinguishable from the baseline. This sweep itself constitutes an empirical robustness check. In addition, the supplementary material contains a controlled noise-injection study in which Gaussian perturbations are added to the ground-truth masks; the proposed method continues to outperform the unsupervised baseline across moderate noise levels. We will expand the discussion section with a short paragraph that (i) acknowledges the risk of propagating annotation biases and (ii) summarizes the noise-robustness findings. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims with no derivations or self-citations visible

full rationale

The abstract presents an empirical framework for incorporating explanation loss into training, introduces two new metrics (annotation coverage and saliency precision), and reports experimental outcomes on chest X-ray datasets. No equations, derivations, fitted parameters, or self-citations appear in the text. The central claims rest on quantitative results rather than any mathematical reduction or uniqueness theorem, so no step reduces to its inputs by construction. The work is therefore self-contained as a methods-and-results paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only text supplies no explicit free parameters, axioms, or invented entities; the approach implicitly assumes standard supervised learning plus an added explanation term whose weighting is chosen empirically.

pith-pipeline@v0.9.0 · 5487 in / 959 out tokens · 28408 ms · 2026-05-12T02:11:02.716102+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

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IndisputableMonolith/Foundation/ArithmeticFromLogic.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Ltotal = Lbce + α Lexp ... explanation loss formulations (logit-based and probability-based) ... annotation coverage and saliency precision

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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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Reference graph

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