PaCL: Patient-aware contrastive learning through metadata refinement for generalized early disease diagnosis

Area of Science:

• Medical Imaging Analysis
• Machine Learning in Healthcare
• Computer Vision

Background:

• Early disease diagnosis is crucial for effective treatment, particularly in time-sensitive healthcare situations.
• Contrastive learning (CL) shows promise for medical tasks but often neglects valuable patient metadata.
• Existing CL methods can suffer from sampling bias, leading to spurious correlations and unequal performance across subgroups.

Purpose of the Study:

• To introduce a novel contrastive learning approach that integrates clinical information and imaging data.
• To enhance model generalization and interpretability in medical image analysis.
• To address sampling bias and improve subgroup performance in contrastive learning models.

Main Methods:

• Proposed Patient-aware Contrastive Learning (PaCL) framework.
• Incorporated an inter-class separability objective (IeSO) using clinical information.
• Introduced an intra-class diversity objective (IaDO) to prevent class collapse.
• Validated theoretically via causal refinements and empirically on six real-world medical imaging tasks.

Main Results:

• PaCL effectively leverages both clinical metadata and imaging data for improved representations.
• The proposed IeSO and IaDO objectives mitigate sampling bias and enhance model robustness.
• PaCL demonstrated superior performance compared to existing techniques across all tested medical imaging tasks.
• The approach showed effectiveness across ophthalmology, radiology, and dermatology imaging modalities.

Conclusions:

• Patient-aware Contrastive Learning (PaCL) offers a significant advancement for medical image analysis.
• Integrating patient metadata alongside imaging data in CL improves diagnostic accuracy and fairness.
• PaCL provides a more generalizable and interpretable solution for medical diagnostic tasks.