Work

ACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training

Year: 2022

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-031-16443-9_7

Authors: Xiaofeng Liu, Fangxu Xing, Nadya Shusharina, Ruth Lim, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Institutions: Harvard University, Gordon Center for Medical Imaging, Massachusetts General Hospital, University of Southern California

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute on Deafness and Other Communication Disorders, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research

Awards: Using Atlas-driven Imaging for Determining Variations in Velopharyngeal Function Among Children with Cleft Palate and Hypernasal Speech, G4170966788, G4922088058, A holistic approach to identifying functional units of tongue motion during speech, Center for Molecular Imaging Technology & Translation (CMITT)

OpenAlex ID: W4281866233

FWCI: 3.595

Cites: 30

Cited by: 28

Related to: 10