{"id":"f49bfe19-9854-440d-b55f-a7416461bd6a","arxiv_id":"2606.06718","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":1,"one_line_summary":"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.","lead":"The paper proposes MSAIC-Net, a convolutional network using multi-scale atrous branches, channel attention, and imbalance-aware contrastive learning to detect myocardial scar and infarction from ECG signals. A smart generalist might read it because ECG is cheap and common, and improved automated detection in low-data settings could support earlier cardiac screening.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Low-data UVA results may reflect split-specific variance rather than method robustness","rationale":"Reader's weakest assumption on dataset representativeness and lack of tuning details directly matches the experimental robustness gap for the low-data claim. Full text availability does not remove the need for variance reporting in small-cohort medical ML; this is the single most load-bearing point for the outperformance assertion.","tokens_in":1776,"tokens_out":268,"duration_ms":17294,"concrete_test":"Recompute UVA scar classification results using 5 independent random 70/30 patient-stratified splits (same hyperparameters); if mean AUC/F1 improvement over the strongest baseline falls below 3% or std dev exceeds 0.04, the 'particularly pronounced' claim does not hold.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim of pronounced outperformance on the small UVA cohort rests on single reported metrics for myocardial scar classification. In low-sample regimes with class imbalance, performance is sensitive to train/test partitioning, patient stratification, and the weighting of the imbalance-aware contrastive term. Without reported standard deviations across multiple splits, nested cross-validation, or ablation isolating the contrastive component's contribution versus the multi-scale attention backbone, it is unclear whether the gains are reproducible or dataset-specific.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","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.","tokens_in":1877,"tokens_out":386,"duration_ms":18766,"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":[{"comment":"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.","section":"Experimental Evaluation"},{"comment":"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.","section":"Proposed Method and Experiments"}],"minor_comments":[{"comment":"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.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"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.","responses":[{"response":"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_made":"yes","referee_comment":"[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."},{"response":"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_made":"yes","referee_comment":"[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."}],"tokens_in":1400,"tokens_out":402,"duration_ms":23028,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main contribution is a named architecture that stacks parallel atrous convolutions, channel attention, and a supervised contrastive term tuned for class imbalance, then adds lead-wise permutation importance. The contrastive piece is the clearest incremental move for this ECG task, since it directly targets the separation of abnormal versus normal samples under imbalance.\n\nIt does a reasonable job of picking two datasets that bracket the problem: the large PTB-XL set and a small institutional UVA cohort where data scarcity is realistic. The claim of larger relative gains on the small set is plausible given how contrastive losses can help when positive examples are few.\n\nThe soft spot is exactly the one the stress-test flags. On the UVA cohort the improvements are described as pronounced, yet the abstract and the available description give no standard deviations across splits, no nested CV, and no ablation that isolates the contrastive term from the multi-scale backbone. In low-sample, imbalanced settings a single train-test partition can produce optimistic numbers; without those checks it is hard to know whether the method is robust or just lucky on that draw.\n\nThe rest of the pipeline looks standard and the interpretability addition is lightweight but useful. No equations collapse into tautologies and the citations track the usual DL-for-biosignals line.\n\nThis is for groups already working on ECG classification who need something that handles imbalance without heavy data augmentation. It is worth sending to peer review so the experimental controls can be tightened; the idea is concrete enough that referees can give targeted feedback on the splits and ablations.","headline":"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.","tokens_in":2372,"tokens_out":404,"would_cite":false,"duration_ms":17135,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"MSAIC-Net combines multi-scale attention with imbalance-aware contrastive learning to improve ECG detection of myocardial scar and infarction.","keywords":["ECG classification","myocardial scar","myocardial infarction","contrastive learning","attention mechanism","multi-scale convolution","class imbalance","model interpretability"],"falsifier":"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.","tokens_in":2692,"feed_emoji":"🫀","tokens_out":749,"duration_ms":30198,"temperature":0.7,"pith_summary":"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.","feed_headline":"Multi-scale attention network lifts ECG scar detection in small datasets","feed_subtitle":"MSAIC-Net pairs atrous branches and imbalance-aware contrastive loss to outperform baselines when labeled ECG records are few.","key_machinery":"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.","core_discovery":"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","pith_inferences":["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."],"forward_implications":["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."],"fun_headline_variants":["MSAIC-Net outperforms ECG scar detection baselines in small datasets","Atrous multi-scale branches capture ECG temporal patterns with attention","Contrastive learning improves feature separability for imbalanced ECG classes","Lead-wise importance enhances interpretability of multi-scale ECG model"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The two chosen datasets capture enough real-world ECG variation that performance gains on them indicate the method will work more broadly.","fun_headline_variants_meta":{"raw":{"variants":["MSAIC-Net outperforms ECG scar detection baselines in small datasets","Atrous multi-scale branches capture ECG temporal patterns with attention","Contrastive learning improves feature separability for imbalanced ECG classes","Lead-wise importance enhances interpretability of multi-scale ECG model"]},"model":"grok-4.3","cost_usd":0.008809,"raw_usage":{"total_tokens":4016,"prompt_tokens":770,"num_sources_used":0,"completion_tokens":67,"cost_in_usd_ticks":88087000,"prompt_tokens_details":{"text_tokens":770,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3179,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":770,"tokens_out":67,"duration_ms":33442,"temperature":1.0,"reasoning_tokens":3179,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T02:15:13.993279+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"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.","supporting_citations":[],"review_version":1}