arxiv: 2604.08502 · v1 · submitted 2026-04-09 · 💻 cs.CV · cs.AI

Recognition: no theorem link

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification

Kabilan Elangovan , Daniel Ting

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:17 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords C-ScoreCAMexplanation consistencymedical image classificationmodel stabilitychest X-rayGradCAMScoreCAM

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The pith

The C-Score quantifies whether medical image classifiers apply the same spatial reasoning to every patient with a given condition and flags instability before accuracy metrics fail.

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

The paper introduces the C-Score to measure if explanation maps from deep learning models stay consistent across different patients who have the same pathology. Standard checks focus only on whether those maps match radiologist annotations for correctness, but leave unexamined whether the model repeats the same visual strategy. The score computes a confidence-weighted average of soft overlap between intensity-emphasized maps on correctly classified examples, without any need for annotations. Tracking the score across training epochs reveals cases where high classification performance coexists with declining explanation consistency, creating risks that accuracy numbers alone cannot detect. On chest X-ray data the metric shows this dissociation in multiple CAM techniques and network architectures, with one example of deterioration appearing a full checkpoint before AUC collapse.

Core claim

The C-Score is the average, confidence-weighted soft IoU computed on intensity-emphasised explanation maps produced by a CAM method for all correctly classified instances of a class. Evaluation on six CAM variants and three CNN architectures over thirty epochs of the Kermany chest X-ray dataset identifies three mechanisms by which AUC and explanation consistency can separate: threshold-mediated gold-list collapse, technique-specific attribution collapse at peak AUC, and class-level masking inside global averages. Because these separations are invisible to classification metrics, the C-Score supplies an annotation-free early signal of impending model instability, as illustrated by ScoreCAM on

What carries the argument

The C-Score itself, a confidence-weighted, annotation-free average of pairwise soft IoU on intensity-emphasised CAM maps restricted to correctly classified instances.

If this is right

High AUC can coexist with low explanation consistency, creating deployment risks invisible to standard performance monitoring.
ScoreCAM on ResNet50V2 exhibits detectable consistency deterioration one full checkpoint before catastrophic AUC collapse.
Architecture-specific deployment choices can be informed by explanation quality rather than predictive ranking alone.
Consistency can be monitored continuously without requiring fresh radiologist annotations for every new case.

Where Pith is reading between the lines

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

During model selection, C-Score could be used alongside accuracy to prefer architectures whose explanations remain stable across patients.
The same consistency-tracking approach might apply to other explainability families or to imaging tasks outside chest X-rays.
Models that maintain high C-Score throughout training may prove more robust when inputs shift slightly from the training distribution.

Load-bearing premise

That pairwise soft IoU on intensity-emphasised explanation maps across correctly classified instances actually captures whether the model applies the same spatial reasoning strategy.

What would settle it

Repeated training runs on the same architectures and dataset in which C-Score deterioration for ScoreCAM on ResNet50V2 fails to precede AUC collapse, or in which high C-Score models still produce visibly inconsistent maps on held-out cases.

Figures

Figures reproduced from arXiv: 2604.08502 by Daniel Ting, Kabilan Elangovan.

**Figure 2.** Figure 2: C-Score heatmap comparison at transfer learning end (E20) and fine-tuning end (E30) [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

**Figure 3.** Figure 3: Net C-Score change (E30−E20) by architecture and method. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

**Figure 4.** Figure 4: Per-class C-Score trajectory by architecture. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗

read the original abstract

Class Activation Mapping (CAM) methods are widely used to generate visual explanations for deep learning classifiers in medical imaging. However, existing evaluation frameworks assess whether explanations are correct, measured by localisation fidelity against radiologist annotations, rather than whether they are consistent: whether the model applies the same spatial reasoning strategy across different patients with the same pathology. We propose the C-Score (Consistency Score), a confidence-weighted, annotation-free metric that quantifies intra-class explanation reproducibility via intensity-emphasised pairwise soft IoU across correctly classified instances. We evaluate six CAM techniques: GradCAM, GradCAM++, LayerCAM, EigenCAM, ScoreCAM, and MS GradCAM++ across three CNN architectures (DenseNet201, InceptionV3, ResNet50V2) over thirty training epochs on the Kermany chest X-ray dataset, covering transfer learning and fine-tuning phases. We identify three distinct mechanisms of AUC-consistency dissociation, invisible to standard classification metrics: threshold-mediated gold list collapse, technique-specific attribution collapse at peak AUC, and class-level consistency masking in global aggregation. C-Score provides an early warning signal of impending model instability. ScoreCAM deterioration on ResNet50V2 is detectable one full checkpoint before catastrophic AUC collapse and yields architecture-specific clinical deployment recommendations grounded in explanation quality rather than predictive ranking alone.

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

C-Score gives a practical new handle on explanation consistency in CAMs for medical images, but the early-warning claim for instability does not hold up without controls on the changing set of correct predictions.

read the letter

The paper defines the C-Score as a confidence-weighted, annotation-free measure of how similar CAM explanations look for the same class across correctly classified cases, using intensity-emphasised pairwise soft IoU. It tracks this over training epochs for six CAM variants on three architectures using the Kermany chest X-ray set. That is the main new piece: prior CAM work mostly checked localisation against annotations, while this one checks reproducibility of the spatial reasoning itself and flags three ways AUC and consistency can diverge that standard metrics miss. The evaluation setup is straightforward and covers transfer learning plus fine-tuning phases, which is useful for seeing how explanations evolve in practice. The shift toward consistency as a deployment signal is a reasonable move for medical imaging, where you want the model to apply the same logic to similar cases rather than just hit high accuracy on average. The soft spot sits in the strongest claim. The abstract states that ScoreCAM on ResNet50V2 shows C-Score deterioration one checkpoint before AUC collapse, yet nothing indicates they held the pool of correctly classified instances fixed or balanced their difficulty across epochs. If easier cases start dominating the correct set later in training, the pairwise IoU can drop for reasons unrelated to lost reasoning consistency. That leaves the precedence non-diagnostic. The three dissociation mechanisms are plausible on paper but rest on the same untested ground. This work is aimed at people building or auditing CAM-based systems for medical classification who need metrics beyond localisation scores. A reader focused on practical evaluation tools would get value from the metric definition and the multi-method comparison. It deserves a serious referee because the core construction is clean and the experimental range is reasonable, even though the instability warning requires ablations on instance subsets to stand. I would send it to review with that specific request.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces the C-Score, a confidence-weighted annotation-free metric that quantifies intra-class explanation consistency for CAM methods via intensity-emphasised pairwise soft IoU computed only on correctly classified instances. It evaluates six CAM techniques (GradCAM, GradCAM++, LayerCAM, EigenCAM, ScoreCAM, MS GradCAM++) across DenseNet201, InceptionV3 and ResNet50V2 on the Kermany chest X-ray dataset over 30 training epochs, identifies three mechanisms of AUC-consistency dissociation, and claims that C-Score deterioration on ResNet50V2 with ScoreCAM provides an early warning of impending AUC collapse one checkpoint prior.

Significance. If the temporal precedence holds after controlling for shifts in the correctly-classified instance pool, C-Score would supply a practical, annotation-free monitor of explanation stability that complements standard classification metrics and could inform architecture-specific deployment decisions in medical imaging. The multi-epoch, multi-architecture evaluation and explicit identification of dissociation mechanisms are strengths that go beyond single-snapshot localisation fidelity studies.

major comments (1)

[Results (ResNet50V2/ScoreCAM)] Results section (ResNet50V2/ScoreCAM early-warning experiment): the claim that C-Score drop precedes catastrophic AUC collapse rests on pairwise soft IoU computed over the changing set of correctly classified instances at each checkpoint. No ablation is described that holds the instance set fixed across epochs or matches class-balance and difficulty statistics to earlier checkpoints; without this control the observed IoU deterioration could arise from a shift toward easier cases rather than loss of consistent spatial reasoning, undermining the early-warning interpretation.

minor comments (2)

[Methods] The exact mathematical definition of the confidence-weighted soft IoU (including the intensity-emphasis transformation and the aggregation over pairs) should be stated explicitly with equation numbers in the Methods section so that the metric can be reproduced without ambiguity.
[Figures] All C-Score and AUC plots over epochs should include per-checkpoint standard deviations or bootstrap confidence intervals and the number of correctly classified instances used at each point.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive review of our manuscript. The concern regarding potential confounding from the evolving set of correctly classified instances in the ResNet50V2/ScoreCAM early-warning analysis is well-taken, and we address it directly below with a commitment to strengthen the supporting evidence.

read point-by-point responses

Referee: Results section (ResNet50V2/ScoreCAM early-warning experiment): the claim that C-Score drop precedes catastrophic AUC collapse rests on pairwise soft IoU computed over the changing set of correctly classified instances at each checkpoint. No ablation is described that holds the instance set fixed across epochs or matches class-balance and difficulty statistics to earlier checkpoints; without this control the observed IoU deterioration could arise from a shift toward easier cases rather than loss of consistent spatial reasoning, undermining the early-warning interpretation.

Authors: We agree that the dynamic nature of the correctly-classified instance pool introduces a potential confound that must be explicitly controlled before the temporal precedence claim can be considered robust. In the revised manuscript we will add a controlled ablation that recomputes C-Score trajectories on a fixed reference set: specifically, the subset of test instances that remain correctly classified from the epoch of peak AUC through the checkpoint immediately preceding the observed collapse. We will additionally report a matched-difficulty variant that subsamples instances at each epoch to preserve the same distribution of prediction confidences as the reference set. These analyses will be presented alongside the original curves so readers can directly assess whether the C-Score decline persists when the evaluated population is held constant. We believe this addition will eliminate the alternative explanation of instance-pool shift while preserving the annotation-free character of the metric. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in metric definition or empirical claims

full rationale

The C-Score is introduced as a direct definition: a confidence-weighted, annotation-free metric computed from intensity-emphasised pairwise soft IoU on CAM explanation maps restricted to correctly classified instances. This construction uses standard similarity measures on model outputs without any fitted parameters, self-referential loops, or reduction of the metric itself to its claimed downstream uses. The reported dissociation mechanisms and early-warning observation on ResNet50V2/ScoreCAM are presented as empirical findings from the thirty-epoch evaluation on the Kermany dataset across six CAM methods and three architectures; they do not rely on self-citations for justification of the metric or invoke uniqueness theorems. No step in the provided abstract or summary equates a prediction or result to its own inputs by construction, and the derivation chain remains 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 soft IoU of CAM maps measures 'spatial reasoning strategy' and that consistency predicts stability; no free parameters are explicitly named in the abstract but the confidence weighting and intensity emphasis are introduced without external validation.

axioms (1)

domain assumption Soft IoU on intensity-weighted CAM maps is a valid proxy for whether the model applies the same spatial reasoning across instances of the same class.
Invoked in the definition of C-Score and the interpretation of its dissociation from AUC.

invented entities (1)

C-Score no independent evidence
purpose: Annotation-free quantification of intra-class explanation reproducibility
Newly defined metric combining confidence weighting and pairwise soft IoU; no independent evidence provided outside the paper.

pith-pipeline@v0.9.0 · 5535 in / 1427 out tokens · 60108 ms · 2026-05-10T18:17:00.709141+00:00 · methodology

discussion (0)

Reference graph

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