Biomedical Image
Biomedical image analysis focuses on extracting meaningful information from medical images to aid diagnosis, treatment planning, and research. Current research emphasizes developing and adapting advanced deep learning models, such as Vision Transformers, YOLO-based architectures, and diffusion models (including Stable Diffusion and Segment Anything Model variants), to improve image segmentation, object detection, and classification tasks. These efforts aim to overcome challenges like data scarcity, annotation limitations, and the need for robust, explainable AI, ultimately improving the accuracy and efficiency of medical image interpretation and impacting patient care.
Papers
Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process
Tianyu Lin, Zhiguang Chen, Zhonghao Yan, Weijiang Yu, Fudan Zheng
A Refer-and-Ground Multimodal Large Language Model for Biomedicine
Xiaoshuang Huang, Haifeng Huang, Lingdong Shen, Yehui Yang, Fangxin Shang, Junwei Liu, Jia Liu