Xuming An, Jacqueline Chua, Yujin Wang, Ruben Hemelings, Rahat Husain, Rachel Chong, Tina Wong, Tin Aung, Damon Wong, Chen Zhang, Leopold Schmetterer
{"title":"解决青光眼结构-功能关系:一个多模态和非配对数据的多任务学习框架","authors":"Xuming An, Jacqueline Chua, Yujin Wang, Ruben Hemelings, Rahat Husain, Rachel Chong, Tina Wong, Tin Aung, Damon Wong, Chen Zhang, Leopold Schmetterer","doi":"10.1109/tmi.2025.3600311","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"8 1","pages":""},"PeriodicalIF":9.8000,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Addressing Glaucoma Structure-Function Relationship: A Multi-Task Learning Framework with Multi-Modal and Unpaired Data\",\"authors\":\"Xuming An, Jacqueline Chua, Yujin Wang, Ruben Hemelings, Rahat Husain, Rachel Chong, Tina Wong, Tin Aung, Damon Wong, Chen Zhang, Leopold Schmetterer\",\"doi\":\"10.1109/tmi.2025.3600311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":13418,\"journal\":{\"name\":\"IEEE Transactions on Medical Imaging\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":9.8000,\"publicationDate\":\"2025-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Medical Imaging\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/tmi.2025.3600311\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Medical Imaging","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/tmi.2025.3600311","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.