Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos
超越IUGC基准：重新思考深度学习方法在胎儿超声视频中的产程超声生物测量需求
机构 * Department of Cardiovascular Surgery, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China ； Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand ； School of Computer Science, University of Sydney, Sydney, Australia ； Neonatology, Sydney Medical School Nepean, University of Sydney Nepean Hospital, Penrith, New South Wales, Australia ； Discipline of Medical Imaging, Faculty of Medicine ； Health, Susan Wakil Health Building, University of Sydney, Camperdown, New South Wales, Australia ； Medical Imaging, Orange Health Service, Orange, New South Wales, Australia ； University of Electronic Science ； Henan Kaifeng College of Science Technology ； Changchun University of Science ； University of Western Brittany, Brest, France ； Sichuan University, Chengdu, China ； Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Masdar, Abu Dhabi ； College of Computer Science ； Engineering, Chongqing University of Technology, Chongqing, China ； Oxford Machine Learning in NeuroImaging Lab, Department of Computer Science, University of Oxford, Oxford, United Kingdom ； Visual Geometry Group, University of Oxford, Oxford, United Kingdom ； School of Computer Science ； Engineering, Nanyang Technological University, Singapore ； The University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China ； College of Computer ； Information Science, Chongqing Normal University, Chongqing, China ； The University of Manchester, Manchester, United Kingdom ； Southwest University, Chongqing, China ； Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany ； Department of Electronic ； Computer Engineering, The Hong Kong University of Science ； Chief Medical Officer Deepecho Ibn Rochd CHU, Hassan II University, Casablanca, Morocco ； Department of Radiography, School of Biomedical ； Allied Health Sciences, College of Health Sciences, University of Ghana, Accra ； Department of Human Biology, Biomedical Engineering Research Center, University of Cape Town, Cape Town, South Africa ； Gynecology Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China ； Department of Obstetrics ； Gynecology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China ； Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, China ； Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China ； Engineering Division, King Abdullah University of Science ； Shenzhen University, Shenzhen, China ； Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain ； School of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
AI总结 本研究提出了一种多任务自动测量框架，用于产程超声生物测量，旨在解决资源有限环境下超声技师短缺的问题，并通过公开数据集和基准结果促进该领域的发展。
