LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task
This work addresses the underexplored application of vision-language models in medical imaging, specifically for chest X-ray analysis, though it appears incremental as it builds on existing models with enhancements.
The authors tackled the problem of applying vision-language models to medical imaging by proposing LiteGPT, a unified framework for joint localization and classification in chest X-rays, achieving new state-of-the-art performance on the VinDr-CXR dataset.
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