qhjqhj00 ecg-linear-zero-shot-eval
Learning ECG Image Representations via Dual Physiological-Aware Alignments — Pham et al. (2026) (arXiv:2604.01526, 2026)
What this evaluates
Evaluates the quality of self-supervised ECG image representations by measuring classification performance under linear probing and zero-shot settings across multiple clinical ECG datasets.
Datasets
- PTB-XL — total ?; splits: test (-1)
- CSN — total ?; splits: test (-1)
- CPSC2018 — total ?; splits: test (-1)
- CODE-test — total ?; splits: test (-1)
Metrics
AUC (in %)(primary) — range: percent- Area under the Receiver Operating Characteristic curve. Computed per diagnostic category and averaged across classes for multi-label classification.
Input / output format
Input: 12-lead ECG images (10 seconds at 500 Hz, pre-generated from signals with varying layouts/resolutions/noise) and corresponding diagnostic labels.
Output: Classification predictions (multi-label probabilities) for linear probing, or similarity scores between image and text embeddings for zero-shot classification.
Scoring recipe
def compute_auc(y_true, y_pred):
auc_scores = []
for i in range(y_true.shape[1]):
auc_scores.append(roc_auc_score(y_true[:, i], y_pred[:, i]))
return np.mean(auc_scores) * 100
Common pitfalls
- PTB-XL contains four independent label types (super-class, sub-class, form, rhythm) that must be evaluated separately.
- Linear probing results are highly sensitive to the specified training set sizes (1%, 10%, 100%), requiring strict data splitting.
- Zero-shot evaluation requires matching image embeddings to text embeddings of diagnostic categories, which depends on prompt construction.
Evidence (verbatim from paper)
performance is reported using AUC (in %) under different training sizes (1%, 10%, and 100%) on PTB-XL, CSN, and CPSC2018. we also assess zero-shot classification (using AUC in %) on PTB-XL, CSN, CPSC2018, and CODE-test
Citation
@misc{pham2026ecgscan,
title={Learning ECG Image Representations via Dual Physiological-Aware Alignments},
author={Pham et al. (2026)},
year={2026},
note={arXiv:2604.01526}
}
- arXiv: 2604.01526