Summary of Improving Medical Multi-modal Contrastive Learning with Expert Annotations, by Yogesh Kumar et al.
Improving Medical Multi-modal Contrastive Learning with Expert Annotations
by Yogesh Kumar, Pekka Marttinen
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an enhanced version of the CLIP model called eCLIP, which integrates expert annotations from radiologist eye-gaze heatmaps to address key challenges in contrastive multi-modal medical imaging analysis. eCLIP tackles data scarcity and the “modality gap” by incorporating a heatmap processor and mixup augmentation, allowing it to efficiently utilize limited expert annotations and improve learning effectiveness. The model is designed to be generally applicable to any variant of CLIP without requiring modifications to its core architecture. Through evaluations across several tasks, including zero-shot inference, linear probing, cross-modal retrieval, and RAG of radiology reports using a frozen Large Language Model, eCLIP demonstrates consistent improvements in embedding quality, showcasing enhanced alignment and uniformity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary eCLIP is a new model that helps doctors better understand medical images. It uses special heatmaps to make the images more useful for computers to analyze. This makes it easier for computers to help doctors with tasks like recognizing different parts of an image or generating reports about what they see. The paper shows that eCLIP works well and can be used in many different ways. |
Keywords
* Artificial intelligence * Alignment * Embedding * Inference * Large language model * Multi modal * Rag * Zero shot
