Human-like visual computing advances explainability and few-shot learning in deep neural networks for complex physiological data

TL;DR

This paper demonstrates that a perception-informed pseudo-colouring technique enhances explainability and enables few-shot learning in deep neural networks analyzing complex physiological data, especially in data-scarce clinical scenarios.

Contribution

It introduces a novel pseudo-colouring approach that encodes clinical features into images, improving interpretability and data efficiency in deep learning models for ECG analysis.

Findings

Pseudo-colouring improves model focus on meaningful ECG features.

Models achieve accurate classification with as few as one or five examples.

Aggregating multiple heartbeats enhances model performance.

Abstract

Machine vision models, particularly deep neural networks, are increasingly applied to physiological signal interpretation, including electrocardiography (ECG), yet they typically require large training datasets and offer limited insight into the causal features underlying their predictions. This lack of data efficiency and interpretability constrains their clinical reliability and alignment with human reasoning. Here, we show that a perception-informed pseudo-colouring technique, previously demonstrated to enhance human ECG interpretation, can improve both explainability and few-shot learning in deep neural networks analysing complex physiological data. We focus on acquired, drug-induced long QT syndrome (LQTS) as a challenging case study characterised by heterogeneous signal morphology, variable heart rate, and scarce positive cases associated with life-threatening arrhythmias such as torsades de pointes. This setting provides a stringent test of model generalisation under extreme data scarcity. By encoding clinically salient temporal features, such as QT-interval duration, into structured colour representations, models learn discriminative and interpretable features from as few as one or five training examples. Using prototypical networks and a ResNet-18 architecture, we evaluate one-shot and few-shot learning on ECG images derived from single cardiac cycles and full 10-second rhythms. Explainability analyses show that pseudo-colouring guides attention toward clinically meaningful ECG features while suppressing irrelevant signal components. Aggregating multiple cardiac cycles further improves performance, mirroring human perceptual averaging across heartbeats. Together, these findings demonstrate that human-like perceptual encoding can bridge data efficiency, explainability, and causal reasoning in medical machine intelligence.