Multi-task Cross-modal Learning for Chest X-ray Image Retrieval

• URL: http://arxiv.org/abs/2601.05399v1
• Date: Thu, 08 Jan 2026 21:44:00 GMT
• Title: Multi-task Cross-modal Learning for Chest X-ray Image Retrieval
• Authors: Zhaohui Liang, Sivaramakrishnan Rajaraman, Niccolo Marini, Zhiyun Xue, Sameer Antani,
• Abstract summary: We propose a multi-task learning framework to fine-tune CLIP and BiomedCLIP for medical retrieval tasks.<n>We show that the fine-tuned model achieves more balanced and clinically meaningful performance across both image-to-text and text-to-image retrieval tasks.<n>These findings highlight the value of domain-adaptive, multi-task learning for advancing cross-modal retrieval in biomedical applications.
• Score: 1.8648093673053043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CLIP and BiomedCLIP are examples of vision-language foundation models and offer strong cross-modal embeddings; however, they are not optimized for fine-grained medical retrieval tasks, such as retrieving clinically relevant radiology reports using chest X-ray (CXR) image queries. To address this shortcoming, we propose a multi-task learning framework to fine-tune BiomedCLIP and evaluate improvements to CXR image-text retrieval. Using BiomedCLIP as the backbone, we incorporate a lightweight MLP projector head trained with a multi-task composite loss function that includes: (1) a binary cross-entropy loss to distinguish normal from abnormal CXR studies, (2) a supervised contrastive loss to reinforce intra-class consistency, and (3) a CLIP loss to maintain cross-modal alignment. Experimental results demonstrate that the fine-tuned model achieves more balanced and clinically meaningful performance across both image-to-text and text-to-image retrieval tasks compared to the pretrained BiomedCLIP and general-purpose CLIP models. Furthermore, t-SNE visualizations reveal clearer semantic clustering of normal and abnormal cases, demonstrating the model's enhanced diagnostic sensitivity. These findings highlight the value of domain-adaptive, multi-task learning for advancing cross-modal retrieval in biomedical applications.

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