在美国,深度学习辅助的乳腺病变诊断图像:一个多供应商,多中心研究

Huiling Xiang¹, Xi Wang¹, Min Xu¹+15

• ¹From the Departments of Ultrasound (H.X., C.L., L.L., T.D., C.Y., J.O., Q.L., A.L., X.L.) and Pathology (J.H., P.S.), Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Zhejiang Laboratory, Hangzhou, China (X.W.); Department of Radiation Oncology, Stanford University School of Medicine, Stanford, Palo Alto, Calif (X.W.); Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China (X.W., P.A.H.); Department of Ultrasound Medicine, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China (M.X.); Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province, Hangzhou, China (M.X.); Department of Ultrasound Medicine, The Third People's Hospital of Zhengzhou, Cancer Hospital of Henan University, Zhengzhou, China (Y.Z.); Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (S.Z.); Department of Ultrasound and Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China (G.T.); and Department of Computer Science and Engineering and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China (H.C.).