LEARNER: Contrastive Pretraining for Learning Fine-Grained Patient Progression from Coarse Inter-Patient Labels

TL;DR

LEARNER introduces a contrastive pretraining method that uses coarse inter-patient labels to learn detailed patient-specific representations, improving prediction of subtle disease progression in medical imaging.

Contribution

This work presents a novel contrastive learning framework that leverages inter-patient variability as a proxy for intra-patient progression, enabling fine-grained modeling from coarse labels.

Findings

Improved classification accuracy on lung ultrasound and brain MRI datasets.

Contrastive pretraining outperforms standard MSE pretraining.

Effective capture of subtle intra-patient changes related to treatment response.

Abstract

Predicting whether a treatment leads to meaningful improvement is a central challenge in personalized medicine, particularly when disease progression manifests as subtle visual changes over time. While data-driven deep learning (DL) offers a promising route to automate such predictions, acquiring large-scale longitudinal data for each individual patient remains impractical. To address this limitation, we explore whether inter-patient variability can serve as a proxy for learning intra-patient progression. We propose LEARNER, a contrastive pretraining framework that leverages coarsely labeled inter-patient data to learn fine-grained, patient-specific representations. Using lung ultrasound (LUS) and brain MRI datasets, we demonstrate that contrastive objectives trained on coarse inter-patient differences enable models to capture subtle intra-patient changes associated with treatment response. Across both modalities, our approach improves downstream classification accuracy and F1-score compared to standard MSE pretraining, highlighting the potential of inter-patient contrastive learning for individualized outcome prediction.