RadCLIP: Enhancing Radiologic Image Analysis through Contrastive Language-Image Pre-training

• URL: http://arxiv.org/abs/2403.09948v2
• Date: Thu, 5 Sep 2024 18:13:16 GMT
• Title: RadCLIP: Enhancing Radiologic Image Analysis through Contrastive Language-Image Pre-training
• Authors: Zhixiu Lu, Hailong Li, Nehal A. Parikh, Jonathan R. Dillman, Lili He,
• Abstract summary: Radiologic Contrastive Language-Image Pre-training (RadCLIP) is a vision-language foundational model that harnesses Vision Language Pre-training framework to improve radiologic image analysis. RadCLIP was pre-trained to align radiologic images with their corresponding text annotations, creating a robust vision backbone for radiologic images. Our Key contributions include curating a large dataset with diverse radiologic 2D/3D radiologic image-text pairs, a slice pooling adapter using an attention mechanism for integrating 2D images, and comprehensive evaluations of RadCLIP on various radiologic downstream tasks.
• Score: 0.1398098625978622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of artificial intelligence (AI) with radiology marks a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the distinct complexities of radiologic 2D and 3D radiologic data pose unique challenges that existing models, pre-trained on general non-medical images, fail to address adequately. To bridge this gap and capitalize on the diagnostic precision required in radiologic imaging, we introduce Radiologic Contrastive Language-Image Pre-training (RadCLIP): a cross-modal vision-language foundational model that harnesses Vision Language Pre-training (VLP) framework to improve radiologic image analysis. Building upon Contrastive Language-Image Pre-training (CLIP), RadCLIP incorporates a slice pooling mechanism tailored for volumetric image analysis and is pre-trained using a large and diverse dataset of radiologic image-text pairs. The RadCLIP was pre-trained to effectively align radiologic images with their corresponding text annotations, creating a robust vision backbone for radiologic images. Extensive experiments demonstrate RadCLIP's superior performance in both uni-modal radiologic image classification and cross-modal image-text matching, highlighting its significant promise for improving diagnostic accuracy and efficiency in clinical settings. Our Key contributions include curating a large dataset with diverse radiologic 2D/3D radiologic image-text pairs, a slice pooling adapter using an attention mechanism for integrating 2D images, and comprehensive evaluations of RadCLIP on various radiologic downstream tasks.

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