Improving Medical Multi-modal Contrastive Learning with Expert Annotations
This work addresses challenges in medical imaging analysis for healthcare applications, but it is incremental as it builds on the existing CLIP framework.
The paper tackled the problem of data scarcity and the modality gap in multi-modal medical imaging analysis by introducing eCLIP, an enhanced CLIP model that integrates radiologist eye-gaze heatmaps, resulting in consistent improvements in embedding quality across tasks like zero-shot inference and cross-modal retrieval.
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.