Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
This work addresses inconsistent interpretations in medical ultrasound analysis for clinicians, though it is incremental as it adapts existing foundation models rather than creating new ones.
This research tackles the labor-intensive task of manually contouring regions of interest in medical ultrasound images by developing domain adaptation methods for vision-language foundation models, achieving state-of-the-art performance on six ultrasound datasets for segmentation and classification tasks.
Medical ultrasonography is an essential imaging technique for examining superficial organs and tissues, including lymph nodes, breast, and thyroid. It employs high-frequency ultrasound waves to generate detailed images of the internal structures of the human body. However, manually contouring regions of interest in these images is a labor-intensive task that demands expertise and often results in inconsistent interpretations among individuals. Vision-language foundation models, which have excelled in various computer vision applications, present new opportunities for enhancing ultrasound image analysis. Yet, their performance is hindered by the significant differences between natural and medical imaging domains. This research seeks to overcome these challenges by developing domain adaptation methods for vision-language foundation models. In this study, we explore the fine-tuning pipeline for vision-language foundation models by utilizing large language model as text refiner with special-designed adaptation strategies and task-driven heads. Our approach has been extensively evaluated on six ultrasound datasets and two tasks: segmentation and classification. The experimental results show that our method can effectively improve the performance of vision-language foundation models for ultrasound image analysis, and outperform the existing state-of-the-art vision-language and pure foundation models. The source code of this study is available at https://github.com/jinggqu/NextGen-UIA.