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REVIEW 2 major objections 1 minor 76 references

MSAIC-Net combines multi-scale attention with imbalance-aware contrastive learning to improve ECG detection of myocardial scar and infarction.

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MSAIC-Net combines parallel atrous convolutions, channel attention, and supervised contrastive learning to outperform baselines on ECG-based myocardial substrate abnormality detection, with larger gains in a low-data institutional cohort.

T0 review reviewed 2026-06-28 challenge →

load-bearing objection MSAIC-Net pairs multi-scale atrous branches and channel attention with a new imbalance-aware contrastive loss; it reports gains on a small clinical ECG cohort but the low-data results rest on single splits without reported variance. the 2 major comments →

arxiv 2606.06718 v1 pith:V3GIR33M submitted 2026-06-04 cs.LG cs.AIcs.SYeess.SY

MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection

Canyu Lei , Fenglin Zhang , Derek Bivona , Cristiane Singulane , Jonathan Pan , Kenneth Bilchick , Amit R. Patel , Jianxin Xie This is my paper
classification cs.LG cs.AIcs.SYeess.SY
keywords ECG classificationmyocardial scarmyocardial infarctioncontrastive learningattention mechanismmulti-scale convolutionclass imbalancemodel interpretability
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 reading

The paper proposes MSAIC-Net to detect myocardial substrate abnormalities such as scar and infarction from ECG signals. It builds parallel atrous convolutional branches to capture features over different time scales, applies channel attention to emphasize useful leads and features, and adds a supervised contrastive loss that pulls same-class samples together while pushing abnormal and normal ones apart despite class imbalance. The method is tested on a small institutional UVA dataset for scar classification and the larger PTB-XL set for MI identification, where it beats standard models with the biggest gains in the low-data case. It also reports lead-wise permutation importance to show which ECG leads matter most. A reader would care because ECG is cheap and common, so an approach that works with less data and gives some lead-level insight could make earlier detection more practical in real clinics.

Core claim

MSAIC-Net employs parallel atrous convolutional branches to extract ECG features across multiple temporal receptive fields, uses channel attention to adaptively reweight informative lead-wise and feature-channel representations, and introduces an imbalance-aware supervised contrastive learning strategy that encourages compact representations within each class while increasing separation between abnormal and normal samples. Lead-wise permutation importance is added to quantify each lead's contribution. On the low-data UVA cohort the model improves myocardial scar classification, and on PTB-XL it improves MI identification, with larger gains in the smaller dataset compared with baseline models

What carries the argument

MSAIC-Net, which integrates parallel atrous convolutional branches for multi-scale feature extraction, channel attention for lead and feature reweighting, and an imbalance-aware supervised contrastive loss to improve class separability in multi-lead ECG signals.

Load-bearing premise

The two chosen datasets capture enough real-world ECG variation that performance gains on them indicate the method will work more broadly.

What would settle it

Testing MSAIC-Net on a third independent multi-lead ECG dataset for scar or MI detection and finding no accuracy gain over standard convolutional networks would show the claimed advantage does not hold.

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

If this is right

  • The network achieves higher accuracy than baselines for myocardial scar classification when labeled ECG data is limited.
  • It improves identification of myocardial infarction on large public ECG collections.
  • The contrastive component increases separation between normal and abnormal ECG representations despite class imbalance.
  • Lead-wise permutation importance provides a concrete ranking of which ECG leads contribute most to each prediction.

Where Pith is reading between the lines

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

  • The same combination of multi-scale branches and contrastive loss could be tried on other imbalanced physiological signals such as EEG or photoplethysmography.
  • If the method reduces the amount of labeled data needed, it might lower the cost of building new ECG classifiers for rare conditions.
  • Lead importance scores could be compared against clinical guidelines to check whether the model highlights leads that cardiologists already consider diagnostic.
  • Evaluating the model on continuous wearable ECG recordings would test whether the gains survive different noise levels and sampling rates.
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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

2 major / 1 minor

Summary. The paper proposes MSAIC-Net, a multi-scale atrous convolutional network augmented with channel attention and an imbalance-aware supervised contrastive loss, for ECG-based classification of myocardial scar (on a small institutional UVA cohort) and myocardial infarction (on PTB-XL). Lead-wise permutation importance is added for interpretability. The central claim is that the model outperforms baselines, with particularly pronounced gains on the low-data, imbalanced UVA dataset.

Significance. If the performance claims are shown to be robust, the work could contribute a practical approach to ECG analysis under class imbalance and limited samples, with added interpretability via lead importance; the combination of multi-scale features and contrastive regularization addresses real clinical challenges in substrate abnormality detection.

major comments (2)
  1. [Experimental Evaluation] Experimental results (UVA cohort): the reported outperformance for myocardial scar classification rests on single metrics without standard deviations across multiple random or patient-stratified splits, nested cross-validation, or repeated partitioning. In low-sample imbalanced regimes this is required to rule out split-specific variance as the source of the gains.
  2. [Proposed Method and Experiments] Method and ablation (contrastive component): no ablation isolating the imbalance-aware supervised contrastive term from the multi-scale attention backbone is presented, so it is impossible to determine whether the claimed improvements are attributable to the novel contrastive strategy or to the convolutional/attention architecture alone.
minor comments (1)
  1. [Abstract] The abstract states outperformance without any numerical results, baseline names, or statistical tests; adding at least the key metrics and p-values would strengthen the summary.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments, which highlight important aspects of robustness and attribution in our experimental evaluation. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Experimental Evaluation] Experimental results (UVA cohort): the reported outperformance for myocardial scar classification rests on single metrics without standard deviations across multiple random or patient-stratified splits, nested cross-validation, or repeated partitioning. In low-sample imbalanced regimes this is required to rule out split-specific variance as the source of the gains.

    Authors: We agree that single-split reporting is insufficient to establish robustness in the low-sample, imbalanced UVA cohort. In the revised manuscript we will rerun the experiments across five patient-stratified random splits, reporting mean and standard deviation for all metrics (AUROC, AUPRC, F1, sensitivity, specificity). We will also add a brief description of the splitting procedure to ensure reproducibility. revision: yes

  2. Referee: [Proposed Method and Experiments] Method and ablation (contrastive component): no ablation isolating the imbalance-aware supervised contrastive term from the multi-scale attention backbone is presented, so it is impossible to determine whether the claimed improvements are attributable to the novel contrastive strategy or to the convolutional/attention architecture alone.

    Authors: We acknowledge the value of isolating the contribution of the imbalance-aware supervised contrastive loss. In the revision we will add an ablation table that compares (i) the multi-scale attention backbone alone, (ii) the backbone plus standard supervised contrastive loss, and (iii) the full MSAIC-Net with the proposed imbalance-aware term. Results will be reported on both the UVA and PTB-XL datasets to quantify the incremental benefit. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical model proposal with external evaluation

full rationale

The manuscript proposes MSAIC-Net as a composite architecture (parallel atrous convolutions + channel attention + imbalance-aware supervised contrastive loss) and reports empirical performance on two external datasets (UVA cohort and PTB-XL). No equations, uniqueness theorems, or derivations are present that reduce a claimed prediction or result to a fitted quantity or self-citation by construction. The contrastive strategy is introduced as a novel component rather than derived from prior self-cited results. All load-bearing claims are experimental comparisons against baselines, with no self-definitional loops or imported ansatzes. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard deep-learning assumptions plus the untested premise that the proposed contrastive objective reliably improves separability on imbalanced ECG data; no free parameters or invented entities are explicitly quantified in the abstract.

free parameters (1)
  • hyperparameters of the contrastive loss and attention modules
    Standard in any deep network; values not reported in abstract but required for the claimed performance.
axioms (2)
  • domain assumption Channel attention and multi-scale atrous convolutions extract lead-wise and temporal features that are more informative than standard CNN baselines for this task.
    Invoked by the architectural design choice described in the abstract.
  • domain assumption The imbalance-aware supervised contrastive loss increases separation between abnormal and normal classes without introducing new biases.
    Central to the method's handling of class imbalance; assumed to hold on the evaluated datasets.

reviewed 2026-06-28 · how reviews work

0 comments
Cite this review

Pith. "Pith review of MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection." pith.science (2026). https://pith.science/paper/V3GIR33M

@misc{pith2026260606718,
  author       = {Pith},
  title        = {Pith review of: MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V3GIR33M}},
  note         = {Machine review of arXiv:2606.06718}
}
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read the original abstract

Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes. Electrocardiography (ECG) provides a low-cost and widely available tool for detecting these abnormalities, but ECG-based detection remains challenging due to heterogeneous lead-dependent manifestations, high-dimensional multi-lead signals, class imbalance, and the limited interpretability of deep learning models. We propose a multi-scale attention-enhanced convolutional network (MSAIC-Net) for ECG-based myocardial substrate abnormality detection. MSAIC-Net employs parallel atrous convolutional branches to extract ECG features across multiple temporal receptive fields. %, enabling the model to capture both local and longer-range temporal patterns. Channel attention is then used to adaptively reweight informative lead-wise and feature-channel representations. To address class imbalance and improve feature separability, we introduce a novel imbalance-aware supervised contrastive learning strategy that encourages samples from the same class to form compact representations while increasing separation between abnormal and normal samples. Lead-wise permutation importance is further incorporated to quantify the contribution of each ECG lead and improve model interpretability. The proposed method was evaluated on two complementary datasets: a low-data institutional cohort from the University of Virginia (UVA) Health System for myocardial scar classification and the large-scale public PTB-XL dataset from PhysioNet for MI identification. Experimental results show that MSAIC-Net outperforms baseline models, with particularly pronounced improvements in the low-data UVA cohort. Overall, the proposed framework provides an effective and interpretable approach for ECG-based detection of myocardial substrate abnormalities.

Figures

Figures reproduced from arXiv: 2606.06718 by Amit R. Patel, Canyu Lei, Cristiane Singulane, Derek Bivona, Fenglin Zhang, Jianxin Xie, Jonathan Pan, Kenneth Bilchick.

Figure 1
Figure 1. Figure 1: Overview of the proposed MSAIC-Net architecture. [PITH_FULL_IMAGE:figures/full_fig_p011_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Permutation Importance Process. The process of permutation importance is illustrated in [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance comparison of backbone architectures on the UVA dataset. [PITH_FULL_IMAGE:figures/full_fig_p025_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance comparison of backbone architectures on the UVA dataset. Con [PITH_FULL_IMAGE:figures/full_fig_p026_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance comparison of backbone architectures on the PTB-XL dataset. [PITH_FULL_IMAGE:figures/full_fig_p028_4.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance comparison of backbone architectures on the PTB-XL dataset. [PITH_FULL_IMAGE:figures/full_fig_p029_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Lead-wise permutation importance across ECG leads on the UVA dataset. The [PITH_FULL_IMAGE:figures/full_fig_p030_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Lead-wise permutation importance across ECG leads on the PTB-XL dataset. [PITH_FULL_IMAGE:figures/full_fig_p032_6.png] view at source ↗

discussion (0)

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This paper was first reviewed by grok-4.3 on June 28, 2026.