Language Augmentation in CLIP for Improved Anatomy Detection on Multi-modal Medical Images

• URL: http://arxiv.org/abs/2405.20735v1
• Date: Fri, 31 May 2024 09:59:11 GMT
• Title: Language Augmentation in CLIP for Improved Anatomy Detection on Multi-modal Medical Images
• Authors: Mansi Kakkar, Dattesh Shanbhag, Chandan Aladahalli, Gurunath Reddy M,
• Abstract summary: Vision-language models have emerged as a powerful tool for challenging multi-modal classification problem in the medical domain. Existing research has focused on clinical descriptions for specific modalities or body regions, leaving a gap for a model providing entire-body multi-modal descriptions. In this paper, we address this gap by automating the generation of standardized body station(s) and list of organ(s) across the whole body in multi-modal MR and CT radiological images.
• Score: 1.4680035572775536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language models have emerged as a powerful tool for previously challenging multi-modal classification problem in the medical domain. This development has led to the exploration of automated image description generation for multi-modal clinical scans, particularly for radiology report generation. Existing research has focused on clinical descriptions for specific modalities or body regions, leaving a gap for a model providing entire-body multi-modal descriptions. In this paper, we address this gap by automating the generation of standardized body station(s) and list of organ(s) across the whole body in multi-modal MR and CT radiological images. Leveraging the versatility of the Contrastive Language-Image Pre-training (CLIP), we refine and augment the existing approach through multiple experiments, including baseline model fine-tuning, adding station(s) as a superset for better correlation between organs, along with image and language augmentations. Our proposed approach demonstrates 47.6% performance improvement over baseline PubMedCLIP.

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