Jiaping Hu1, Zhao Wang2, Lijie Zhong1, Keyan Yu1, Yanjun Chen1, Yingjie Mei3, Qi Dou4, and Xiaodong Zhang1
1Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China, 2College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China, 3China International Center, Philips Healthcare, Guangzhou, China, 4Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, China
1Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China, 2College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China, 3China International Center, Philips Healthcare, Guangzhou, China, 4Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, China
The
deep learning-based dichotomous model performed can recognize the presence of
BMLs accurately and initially assess of severity, making it meaningful to be a rapid
detector of BMLs for associated research.
Figure 1. Overview
of the proposed BML lesion identification deep learning model trained with
patient-level weak labels.
