Analysis of the Shortcut Learning and Clever Hans Effect in CNN based ECG Image Classification
Deep learning models for ECG image classification may achieve high accuracy by exploiting non-physiological visual cues instead of ECG waveform morphology. Given the black-box nature of deep learning models, their promise of high predictive performance often remains insufficiently translated into clinical or real-world trust, interpretability, and actionable decision-making. In this study, we examine shortcut learning and Clever Hans effect in a publicly available ECG image dataset using convolutional neural networks. In process we have created six image-derived feature sets (FSs), FS1: raw full ECG images, FS2: cropped waveform-only images, FS3: waveform-masked metadata images, FS4: red-...
| Authors | Abhay Kumar Pathak, Mrityunjay Chaubey, Manjari Gupta et al. |
|---|---|
| Primary category | eess.IV |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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