Leveraging the Structure of Medical Data for Improved Representation Learning

2025-07-01

AI Summary

To address weak model generalization caused by scarce annotations and limited sample sizes (e.g., MIMIC-CXR) in medical imaging, this paper proposes a structure-aware self-supervised pretraining framework. Methodologically, it treats paired posteroanterior (PA) and lateral chest radiographs as natural positive samples for contrastive learning—eliminating reliance on textual annotations—and integrates masked image modeling, cross-view representation alignment, and sparse image patch reconstruction to enable lightweight, modality-agnostic domain adaptation. The core contribution lies in explicitly encoding intrinsic geometric and anatomical structural constraints inherent in multi-view medical images. Experiments demonstrate that the proposed approach significantly outperforms both supervised baselines and unstructured self-supervised methods on MIMIC-CXR, validating the critical role of structural priors in improving representation learning for small-sample medical AI.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Building generalizable medical AI systems requires pretraining strategies that are data-efficient and domain-aware. Unlike internet-scale corpora, clinical datasets such as MIMIC-CXR offer limited image counts and scarce annotations, but exhibit rich internal structure through multi-view imaging. We propose a self-supervised framework that leverages the inherent structure of medical datasets. Specifically, we treat paired chest X-rays (i.e., frontal and lateral views) as natural positive pairs, learning to reconstruct each view from sparse patches while aligning their latent embeddings. Our method requires no textual supervision and produces informative representations. Evaluated on MIMIC-CXR, we show strong performance compared to supervised objectives and baselines being trained without leveraging structure. This work provides a lightweight, modality-agnostic blueprint for domain-specific pretraining where data is structured but scarce

Problem

• Improving medical AI with data-efficient pretraining strategies
• Leveraging multi-view X-ray structure for self-supervised learning
• Enhancing representation learning in scarce annotated medical datasets

Innovation

• Self-supervised framework using multi-view X-rays
• Reconstructs views from sparse patches
• Aligns latent embeddings without text supervision