Transfer learning for anatomical structure segmentation in otorhinolaryngology microsurgery.

Journal: The international journal of medical robotics + computer assisted surgery : MRCAS

Published Date: Jun 1, 2024

BACKGROUND: Reducing the annotation burden is an active and meaningful area of artificial intelligence (AI) research.

Xin Ding

Department of Otorhinolaryngology Head and Neck Surgery, the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yu Huang

School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China.
Yang Zhao

The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
Xu Tian

Department of Otorhinolaryngology Head and Neck Surgery, the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Guodong Feng

Department of Otorhinolaryngology Head and Neck Surgery, the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhiqiang Gao

Beijing Entry-Exit Inspection and Quarantine Bureau, Beijing 100026, China.

Algorithms Anatomic Landmarks Artificial Intelligence Humans Image Processing, Computer-Assisted Machine Learning Microsurgery Neural Networks, Computer Otorhinolaryngologic Surgical Procedures Surgery, Computer-Assisted

View on PubMed Access via DOI PubMed (38767083)