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arXiv · 2407.12064

LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task

Khai Le-Duc·Ryan Zhang·Ngoc Son Nguyen·Tan-Hanh Pham·Anh Dao·Ba Hung Ngo·Anh Totti Nguyen·Truong-Son Hy
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Abstract

Vision-language models have been extensively explored across a wide range of tasks, achieving satisfactory performance; however, their application in medical imaging remains underexplored. In this work, we propose a unified framework - LiteGPT - for the medical imaging. We leverage multiple pre-trained visual encoders to enrich information and enhance the performance of vision-language models. To the best of our knowledge, this is the first study to utilize vision-language models for the novel task of joint localization and classification in medical images. Besides, we are pioneers in providing baselines for disease localization in chest X-rays. Finally, we set new state-of-the-art performance in the image classification task on the well-benchmarked VinDr-CXR dataset. All code and models are publicly available online: https://github.com/leduckhai/LiteGPT

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Khai Le-Duc, Ryan Zhang, Ngoc Son Nguyen, Tan-Hanh Pham, Anh Dao, Ba Hung Ngo, Anh Totti Nguyen, Truong-Son Hy. 2024-07-16. LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task. https://arxiv.org/abs/2407.12064

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