利用分段任意模型通过双特征引导自动提示进行无源域自适应

IF 9.8 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging Pub Date : 2025-07-15 DOI:10.1109/tmi.2025.3587733

Zheang Huai, Hui Tang, Yi Li, Zhuangzhuang Chen, Xiaomeng Li

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Medical Imaging 医学-成像科学与照相技术

CiteScore

21.80

自引率

5.70%

发文量

637

审稿时长

5.6 months

期刊介绍： The IEEE Transactions on Medical Imaging (T-MI) is a journal that welcomes the submission of manuscripts focusing on various aspects of medical imaging. The journal encourages the exploration of body structure, morphology, and function through different imaging techniques, including ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods. It also promotes contributions related to cell and molecular imaging, as well as all forms of microscopy. T-MI publishes original research papers that cover a wide range of topics, including but not limited to novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and other related methods. The journal particularly encourages highly technical studies that offer new perspectives. By emphasizing the unification of medicine, biology, and imaging, T-MI seeks to bridge the gap between instrumentation, hardware, software, mathematics, physics, biology, and medicine by introducing new analysis methods. While the journal welcomes strong application papers that describe novel methods, it directs papers that focus solely on important applications using medically adopted or well-established methods without significant innovation in methodology to other journals. T-MI is indexed in Pubmed® and Medline®, which are products of the United States National Library of Medicine.