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

2024-03-15

arXiv.org

AI Summary

Radiological 2D/3D imaging exhibits complex anatomical structures, highly variable lesion scales, and sparse clinical semantics—challenges that general-purpose vision-language pretraining (VLP) models fail to address for high-accuracy diagnosis. To bridge this gap, we propose RadCLIP, the first radiology-specific contrastive vision-language pretraining framework. RadCLIP introduces a radiology-tailored image–text pairing paradigm and constructs a large-scale, multi-center radiology image–report paired dataset. It incorporates an attention-driven 3D slice pooling adapter to enable fine-grained spatial semantic aggregation and jointly optimizes radiological image and clinical text encoders. Extensive experiments demonstrate that RadCLIP significantly outperforms general VLP baselines—including CLIP and FLAVA—on both unimodal classification and cross-modal retrieval tasks. Moreover, it achieves consistent improvements in downstream radiological diagnosis tasks, such as pneumonia detection, nodule classification, and lesion localization, enhancing both accuracy and robustness.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Addressing challenges in radiologic 2D/3D image analysis with AI
• Improving diagnostic precision in radiology using vision-language models
• Bridging the gap between general and medical image pre-training

Innovation

• Uses Vision Language Pre-training for radiologic images
• Incorporates slice pooling for volumetric image analysis
• Pre-trained with diverse radiologic image-text pairs