arXiv subjects

Abhay Kumar Pathak

Publications and source records attributed to Abhay Kumar Pathak.

3 recordsLinked to original sources

arXiv2026

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-arrow artifact images for the myocardial infarction class, FS5: contrast-enhanced images for the abnormal heartbeat class and FS6: Gaussian-blurred images for the normal class. These controlled representations were used to test whether classification performance persists when waveform information is removed or when artificial class-specific artifacts are introduced. Shortcut retention score, prediction consistency and confidence divergence across Feature-Set Representations have been calculated to assess the transparency about the learning pattern. Along with factual results, average Integrated Gradients and occlusion sensitivity test results are presented to inspect whether model attribution focused on ECG-relevant waveform regions or on non-clinical artifacts. Performance changes across feature sets and attribution patterns were used to identify potential Clever Hans behavior. This study evaluates whether ECG image classifiers learn clinically meaningful morphology or shortcut cues introduced by report layout, metadata, contrast, blur, or artificial markers.

eess.IV↗

arXiv2026

ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets

Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly while relying on irrelevant cues, shortcut associations, peripheral structures, or device level artifacts instead of task relevant regions. On large scale datasets this opacity is especially problematic, since inspecting heatmaps one sample at a time cannot scale to thousands of predictions. We propose Relevance Based Model Decision Explainability (ReMoDEx), a framework for systematic, dataset scale assessment of model decision behaviour in image classification. ReMoDEx defines a stepwise pipeline: model inference, target class selection, relevance map generation, heatmap standardisation, similarity based grouping of patterns, cluster level interpretation, and spatial relevance assessment. Local methods GradCAM++, Integrated Gradients, Occlusion Sensitivity, and Layerwise Relevance Propagation are each combined independently with a single global module that summarises an entire set of relevance maps into a few decision strategy clusters, replacing sample by sample inspection with an automatic, scalable summary. To demonstrate ReMoDEx, we applied it to a VGG16 based classifier distinguishing COVID-19, Normal, Lung Opacity, and Viral Pneumonia. The classifier showed stable performance (86.27% test accuracy, 0.9624 test AUC). However, each explainer combined with the global module consistently produced two recurring strategies: central thoracic region decisions and border/corner sensitive decisions, indicating possible shortcut learning that conventional metrics could not reveal. Masked image validation confirmed that model confidence and predicted class changed when central or peripheral regions were occluded. ReMoDEx thus provides a scalable relevance based decision assessment framework and an essential complement to accuracy based evaluation.

cs.CV↗

arXiv2024

Time-Efficient Hybrid Hyperparameter Tuning Approach for Cardiovascular Disease Classification

Cardiovascular diseases (CVDs) are any serious illness of the heart, which require accurate diagnosis to prevent fatal consequences. Hyperparameter tuning plays a critical role in optimizing machine learning model performance by selecting the most suitable parameter configurations for improved accuracy, generalization, and reliability. Grid search systematically evaluates predefined hyperparameter combinations, whereas random search samples configurations randomly from the search space enabling broader exploration with reduced computational cost. Therefore, an efficient tuning strategy is essential when developing classification models where time plays an crucial role along with the predictive capability. In this work, we propose a new hyperparameter tuning approach to tune the hyperparameters of ML models for CVD classification. The proposed random grid search combines the power of random search to explore the global space with the focused and exhaustive search of grid search in the most promising areas. This hybrid approach finds an optimal balance between exploration and exploitation and yields a robust and time-efficient ML model for classification seetings. Experimental results on state of the art models demonstrated that randomised grid search performed better than traditional hyperparameter tuning methods. In addition to the observed improvement in model performance, the computational time required for training models was substantially reduced across most of the models. Presented results of the proposed study emphasizes the reduction in training time and computational efficiency of the proposed Randomized-Grid Search method. The proposed technique has significant potential to advance ML application in healthcare providing timely and accurate CVDs diagnosis.

cs.LG↗