arxiv: 2604.27017 · v1 · submitted 2026-04-29 · 📡 eess.IV · cs.LG· stat.ML

Recognition: unknown

Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution

Karol Dobiczek , Maciej Mozolewski , Szymon Bobek , Micha{\l} Szafarczyk , Peter van Dam , Grzegorz J. Nalepa

Authors on Pith no claims yet

Pith reviewed 2026-05-07 11:34 UTC · model grok-4.3

classification 📡 eess.IV cs.LGstat.ML

keywords ECG analysisfeature attribution3D reconstructioncross-modal mappingclinical interpretabilitydeep learningpathology localization

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The pith

Projecting feature attributions from 12-lead ECG models onto CineECG 3D reconstructions improves alignment with expert-annotated pathological locations.

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

The paper introduces a cross-modal method that takes explanations generated by high-performing models on standard 12-lead ECG signals and projects them into three-dimensional heart anatomy derived from CineECG. This step aims to translate abstract waveform changes into concrete anatomical positions, overcoming the instability of direct attribution techniques that struggle to connect signals to physical heart structures. Direct models trained on CineECG data alone produce lower diagnostic accuracy and less coherent maps. When tested on 20 expert-annotated cases, the mapped attributions reach a Dice score of 0.56, exceeding the 0.47 score from unmapped 12-lead attributions and demonstrating better localization of disease features.

Core claim

While models trained directly on CineECG signals suffer from reduced accuracy and incoherent attributions, the proposed cross-modal averaging mapping effectively recovers clinically relevant feature rankings from standard 12-lead ECG models and achieves a Dice score of 0.56 against expert ground truth, outperforming the 0.47 baseline of standard 12-lead attributions.

What carries the argument

cross-modal averaging mapping that projects feature attributions from 12-lead ECG models onto CineECG 3D anatomical reconstructions

If this is right

The mapping recovers clinically relevant feature rankings even when direct CineECG models underperform.
Cross-modal averaging filters attribution instability and improves localization of pathological features.
The approach combines the diagnostic expressiveness of standard ECG models with the intuitive clarity of anatomical visualization.
Clinical integration of ECG AI becomes more feasible when explanations are grounded in 3D heart anatomy.

Where Pith is reading between the lines

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

Cardiologists could use the 3D visualizations to cross-check AI predictions against visible heart structures during diagnosis.
The method might extend to other signal-to-image modalities for broader multi-modal diagnostic support.
Larger validation cohorts could reveal whether the Dice improvement holds across more varied patient populations and annotation styles.

Load-bearing premise

The 20 expert-annotated cases form a reliable ground-truth set and the CineECG 3D reconstructions accurately capture the anatomical locations relevant to the ECG attributions without systematic mapping errors.

What would settle it

An independent test set of expert-annotated cases in which the mapped attributions show no Dice score improvement or perform worse than standard 12-lead attributions would falsify the claim of improved localization.

Figures

Figures reproduced from arXiv: 2604.27017 by Grzegorz J. Nalepa, Karol Dobiczek, Maciej Mozolewski, Micha{\l} Szafarczyk, Peter van Dam, Szymon Bobek.

**Figure 1.** Figure 1: Separate models are trained on ECG and CineECG data. Model feature view at source ↗

**Figure 2.** Figure 2: XAI-optimized 1D-ResNet architecture designed for variable-length ECG view at source ↗

**Figure 3.** Figure 3: Expert annotation compared to model attributions (IG, absolute values) view at source ↗

**Figure 4.** Figure 4: Comparison of multimodal attributions for case FID 18315. (a) 12-lead view at source ↗

read the original abstract

Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required for clinical integration. Standard feature attribution methods are limited by the inherent difficulty in mapping abstract waveform fluctuations to physical anatomical pathologies. To resolve this, we propose a cross-modal method that projects feature attributions from high-performance 12-lead ECG models onto the CineECG 3D anatomical space. Our study reveals that while models trained directly on CineECG signals suffer from reduced accuracy and incoherent attributions, the proposed mapping mechanism effectively recovers clinically relevant feature rankings. Validated against a ground-truth dataset of 20 cases annotated by domain experts, the mapped explanations yield a Dice score of 0.56, significantly outperforming the 0.47 baseline of standard 12-lead attributions. These findings indicate that cross-modal averaging mapping effectively filters attribution instability and improves the localization of pathological features, combining the diagnostic expressiveness of standard ECG with the intuitive clarity of anatomical visualization.

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

The cross-modal mapping improves Dice scores modestly on 20 cases but the validation lacks checks for annotation reliability and mapping errors.

read the letter

The punchline is that the cross-modal mapping from 12-lead attributions to CineECG 3D space gives a Dice improvement of 0.56 over 0.47 on expert labels, but the whole thing depends on whether those 20 annotations are solid ground truth and whether the reconstruction introduces spatial errors. What the paper does is try to solve the interpretability problem in ECG deep learning by adding anatomical localization. Models on standard 12-lead data are accurate but hard to map to heart locations, while CineECG gives the 3D view but apparently trains less well and has incoherent attributions. The averaging mapping seems to filter some instability. That's a reasonable observation, and validating against external annotations avoids obvious circularity. Where it gets soft is the lack of supporting evidence for the key assumptions. The abstract doesn't report any measure of agreement between the domain experts or any test of how sensitive the Dice score is to small changes in the mapping or reconstruction. With only 20 cases, even a 0.09 difference could be within noise. Details on the architectures, the exact mapping formula, and selection of the validation set are also absent, which makes it tough to judge reproducibility. This kind of work is aimed at the intersection of machine learning and cardiology, particularly people trying to get deep models into clinical workflows where doctors need to see why a prediction was made in terms of actual heart anatomy. A reader who follows ECG AI papers might pick up the idea of cross-modal attribution as something to build on. I think it should go to peer review. The central claim is testable and the motivation is sound, so referees can help strengthen the validation side.

Referee Report

3 major / 0 minor

Summary. The manuscript proposes a cross-modal averaging method to project feature attributions from high-performance 12-lead ECG deep learning models onto CineECG 3D anatomical space. It reports that models trained directly on CineECG signals yield reduced accuracy and incoherent attributions, while the proposed mapping recovers clinically relevant rankings and achieves a Dice score of 0.56 on 20 expert-annotated cases, outperforming the 0.47 baseline of standard 12-lead attributions; the authors conclude that the mapping filters attribution instability and improves localization of pathological features.

Significance. If the validation is shown to be robust, the work could meaningfully advance clinical interpretability of ECG AI by linking waveform attributions to anatomical locations without retraining on 3D data. The separation between model training on standard ECG and external expert validation on CineECG cases is a methodological strength that avoids circularity.

major comments (3)

Abstract: The headline result (Dice 0.56 vs. 0.47) is presented without statistical tests, confidence intervals, or p-values, and without describing how the cross-modal averaging mapping is computed or what controls were applied for selection bias in the 20-case set; these omissions are load-bearing for the central claim that the mapping improves localization.
Validation (20-case ground-truth set): No inter-rater agreement metrics or sensitivity analysis to CineECG 3D reconstruction/registration errors are reported. If either the expert annotations or the projection step introduce spatially correlated noise, the observed Dice gain cannot be attributed to the mapping mechanism.
Methods: The manuscript provides no details on the 12-lead model architectures, the precise projection algorithm (including any parameters or assumptions), or how pathological locations were defined in 3D space for Dice computation; these are required to assess reproducibility and to rule out systematic mapping artifacts.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important aspects for improving the clarity, reproducibility, and robustness of our work. We address each major comment point-by-point below, indicating where revisions have been made to the manuscript.

read point-by-point responses

Referee: Abstract: The headline result (Dice 0.56 vs. 0.47) is presented without statistical tests, confidence intervals, or p-values, and without describing how the cross-modal averaging mapping is computed or what controls were applied for selection bias in the 20-case set; these omissions are load-bearing for the central claim that the mapping improves localization.

Authors: We agree that these details are essential for supporting the central claim. We have revised the abstract to include a paired t-test result (p=0.03) with 95% confidence intervals for the Dice scores (0.56 [0.51-0.61] vs. 0.47 [0.42-0.52]). We have also added a concise description of the cross-modal averaging mapping and clarified that the 20 cases were randomly sampled from the full cohort without additional selection criteria, thereby addressing potential bias concerns. revision: yes
Referee: Validation (20-case ground-truth set): No inter-rater agreement metrics or sensitivity analysis to CineECG 3D reconstruction/registration errors are reported. If either the expert annotations or the projection step introduce spatially correlated noise, the observed Dice gain cannot be attributed to the mapping mechanism.

Authors: We acknowledge the value of inter-rater metrics; however, annotations were performed by a single expert due to clinical time constraints, so these metrics cannot be computed. We have explicitly noted this as a limitation in the revised Discussion. For sensitivity to reconstruction and registration errors, we have added a new analysis in the supplementary material varying registration parameters by ±10% and confirming that the Dice improvement remains statistically significant and consistent, supporting attribution to the mapping mechanism rather than noise. revision: partial
Referee: Methods: The manuscript provides no details on the 12-lead model architectures, the precise projection algorithm (including any parameters or assumptions), or how pathological locations were defined in 3D space for Dice computation; these are required to assess reproducibility and to rule out systematic mapping artifacts.

Authors: We apologize for the insufficient detail in the initial submission. The revised Methods section now specifies the 12-lead model as a ResNet-18 architecture with details on training hyperparameters, describes the projection algorithm as a lead-position-weighted averaging onto the CineECG 3D mesh with explicit parameters (e.g., Gaussian kernel sigma=5mm) and assumptions (standard 12-lead electrode placements), and defines pathological locations as the union of expert-annotated 3D regions corresponding to diagnostic findings. We will also release the projection code publicly to facilitate reproducibility. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation or validation chain

full rationale

The paper proposes a cross-modal projection of 12-lead ECG attributions onto CineECG 3D space and reports an empirical Dice-score improvement (0.56 vs 0.47 baseline) on an external set of 20 expert-annotated cases. No equations, fitted parameters, or self-referential definitions appear in the provided text that would reduce the claimed performance gain to an input by construction. The validation is presented as comparison against independent ground-truth annotations rather than a re-use of training data or a self-citation chain that forbids alternatives. Consequently the central result does not collapse into any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim depends on the assumption that expert annotations on 20 cases constitute unbiased ground truth and that the CineECG 3D model provides a faithful anatomical coordinate system for attribution projection; no free parameters or invented entities are described.

pith-pipeline@v0.9.0 · 5500 in / 1078 out tokens · 39025 ms · 2026-05-07T11:34:45.649175+00:00 · methodology

discussion (0)

Reference graph

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