1 code implementation • 16 Nov 2023 • Bingnan Li, Zhitong Gao, Xuming He
Cross-modal MRI segmentation is of great value for computer-aided medical diagnosis, enabling flexible data acquisition and model generalization.
no code implementations • 27 Sep 2023 • Xuanlong Yu, Yi Zuo, Zitao Wang, Xiaowen Zhang, Jiaxuan Zhao, Yuting Yang, Licheng Jiao, Rui Peng, Xinyi Wang, Junpei Zhang, Kexin Zhang, Fang Liu, Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo, Hanlin Tian, Kenta Matsui, Tianhao Wang, Fahmy Adan, Zhitong Gao, Xuming He, Quentin Bouniot, Hossein Moghaddam, Shyam Nandan Rai, Fabio Cermelli, Carlo Masone, Andrea Pilzer, Elisa Ricci, Andrei Bursuc, Arno Solin, Martin Trapp, Rui Li, Angela Yao, Wenlong Chen, Ivor Simpson, Neill D. F. Campbell, Gianni Franchi
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
1 code implementation • 20 Jun 2023 • Chuanyang Hu, Shipeng Yan, Zhitong Gao, Xuming He
Despite deep learning has achieved great success, it often relies on a large amount of training data with accurate labels, which are expensive and time-consuming to collect.
1 code implementation • 14 Dec 2022 • Zhitong Gao, Yucong Chen, Chuyu Zhang, Xuming He
In this work, we focus on capturing the data-inherent uncertainty (aka aleatoric uncertainty) in segmentation, typically when ambiguities exist in input images.
no code implementations • 24 Jun 2022 • Chuyu Zhang, Chuanyang Hu, Ruijie Xu, Zhitong Gao, Qian He, Xuming He
Our insight is to utilize mutual information to measure the relation between seen classes and unseen classes in a restricted label space and maximizing mutual information promotes transferring semantic knowledge.
1 code implementation • 21 Jul 2021 • Shuailin Li, Zhitong Gao, Xuming He
Learning segmentation from noisy labels is an important task for medical image analysis due to the difficulty in acquiring highquality annotations.