Improving Medical Report Generation with Adapter Tuning and Knowledge Enhancement in Vision-Language Foundation Models

• URL: http://arxiv.org/abs/2312.03970v1
• Date: Thu, 7 Dec 2023 01:01:45 GMT
• Title: Improving Medical Report Generation with Adapter Tuning and Knowledge Enhancement in Vision-Language Foundation Models
• Authors: Shibin Wu, Bang Yang, Zhiyu Ye, Haoqian Wang, Hairong Zheng, Tong Zhang
• Abstract summary: This study builds upon the state-of-the-art vision-language pre-training and fine-tuning approach, BLIP-2, to customize general large-scale foundation models. Validation on the dataset of ImageCLEFmedical 2023 demonstrates our model's prowess, achieving the best-averaged results against several state-of-the-art methods.
• Score: 26.146579369491718
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical report generation demands automatic creation of coherent and precise descriptions for medical images. However, the scarcity of labelled medical image-report pairs poses formidable challenges in developing large-scale neural networks capable of harnessing the potential of artificial intelligence, exemplified by large language models. This study builds upon the state-of-the-art vision-language pre-training and fine-tuning approach, BLIP-2, to customize general large-scale foundation models. Integrating adapter tuning and a medical knowledge enhancement loss, our model significantly improves accuracy and coherence. Validation on the dataset of ImageCLEFmedical 2023 demonstrates our model's prowess, achieving the best-averaged results against several state-of-the-art methods. Significant improvements in ROUGE and CIDEr underscore our method's efficacy, highlighting promising outcomes for the rapid medical-domain adaptation of the vision-language foundation models in addressing challenges posed by data scarcity.

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