Improving Medical Multi-modal Contrastive Learning with Expert Annotations

TL;DR

eCLIP enhances medical multi-modal contrastive learning by integrating expert radiologist annotations, notably eye-gaze heatmaps, to improve embedding quality, address data scarcity, and bridge the modality gap, leading to better cross-modal tasks.

Contribution

The paper introduces eCLIP, a novel method that incorporates expert annotations into CLIP for medical imaging, improving multi-modal representations without altering the core architecture.

Findings

Improved embedding alignment and uniformity across tasks.

Enhanced zero-shot and retrieval performance in medical imaging.

Effective utilization of scarce expert annotations through mixup augmentation.

Abstract

We introduce eCLIP, an enhanced version of the CLIP model that integrates expert annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in contrastive multi-modal medical imaging analysis, notably data scarcity and the "modality gap" -- a significant disparity between image and text embeddings that diminishes the quality of representations and hampers cross-modal interoperability. eCLIP integrates a heatmap processor and leverages mixup augmentation to efficiently utilize the scarce expert annotations, thus boosting the model's learning effectiveness. eCLIP is designed to be generally applicable to any variant of CLIP without requiring any modifications of the core architecture. Through detailed evaluations across several tasks, including zero-shot inference, linear probing, cross-modal retrieval, and Retrieval Augmented Generation (RAG) of radiology reports using a frozen Large Language Model, eCLIP showcases consistent improvements in embedding quality. The outcomes reveal enhanced alignment and uniformity, affirming eCLIP's capability to harness high-quality annotations for enriched multi-modal analysis in the medical imaging domain.