DAAC: Discrepancy-Aware Adaptive Contrastive Learning for Medical Time series

Yifan WANG, Hongfeng Ai, Ruiqi Li, Maowei Jiang, Quangao Liu, Jiahua Dong, Ruiyuan Kang, Alan Liang, Zihang Wang, Ruikai Liu, Cheng Jiang, Chenzhong Li

Keywords: Medical Time Series, Contrastive Learning, Multi-View Representation, Discrepancy Estimation, Disease Diagnosis

TL;DR: A contrastive learning framework integrating discrepancy estimation and adaptive attention for medical time-series diagnosis.

Abstract:

Medical time-series data play a vital role in disease diagnosis but suffer from limited labeled samples and single-center bias, which hinder model generalization and lead to overfitting. To address these challenges, we propose DAAC (Discrepancy-Aware Adaptive Contrastive learning), a learnable multi-view contrastive framework that integrates external normal samples and enhances feature learning through adaptive contrastive strategies. DAAC consists of two key modules: (1) a Discrepancy Estimator, built upon a GAN-enhanced encoder-decoder architecture, captures the distribution of normal data and computes reconstruction errors as indicators of abnormality. These discrepancy features augment the target dataset to mitigate overfitting. (2) an Adaptive Contrastive Learner uses multi-head attention to extract discriminative representations by contrasting embeddings across multiple views and data granularities (subject, trial, epoch, and temporal levels), eliminating the need for handcrafted positive-negative sample pairs. Extensive experiments on three clinical datasets—covering Alzheimer’s disease, Parkinson’s disease, and myocardial infarction—demonstrate that DAAC significantly outperforms existing methods, even when only 10\% of labeled data is available, showing strong generalization and diagnostic performance. Our code is available
at https://github.com/CUHKSZ-MED-BioE/DAAC.

Primary Area: Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Submission Number: 10704