UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities

• URL: http://arxiv.org/abs/2412.10372v1
• Date: Fri, 13 Dec 2024 18:59:40 GMT
• Title: UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities
• Authors: Muhammad Uzair Khattak, Shahina Kunhimon, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan,
• Abstract summary: Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks. UniMed is a large-scale, open-source multi-modal medical dataset comprising over 5.3 million image-text pairs. We trained UniMed-CLIP, a unified VLM for six modalities, achieving notable gains in zero-shot evaluations.
• Score: 68.12889379702824
• License:
• Abstract: Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks. However, their application in the medical domain remains limited due to the scarcity of openly accessible, large-scale medical image-text datasets. Existing medical VLMs either train on closed-source proprietary or relatively small open-source datasets that do not generalize well. Similarly, most models remain specific to a single or limited number of medical imaging domains, again restricting their applicability to other modalities. To address this gap, we introduce UniMed, a large-scale, open-source multi-modal medical dataset comprising over 5.3 million image-text pairs across six diverse imaging modalities: X-ray, CT, MRI, Ultrasound, Pathology, and Fundus. UniMed is developed using a data-collection framework that leverages Large Language Models (LLMs) to transform modality-specific classification datasets into image-text formats while incorporating existing image-text data from the medical domain, facilitating scalable VLM pretraining. Using UniMed, we trained UniMed-CLIP, a unified VLM for six modalities that significantly outperforms existing generalist VLMs and matches modality-specific medical VLMs, achieving notable gains in zero-shot evaluations. For instance, UniMed-CLIP improves over BiomedCLIP (trained on proprietary data) by an absolute gain of +12.61, averaged over 21 datasets, while using 3x less training data. To facilitate future research, we release UniMed dataset, training codes, and models at https://github.com/mbzuai-oryx/UniMed-CLIP.

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