1 code implementation • Submitted to ICLR 2022 • Wentao Zhu, Yufang Huang, Xiufeng Xie, Wenxian Liu, Jincan Deng, Debing Zhang, Zhangyang Wang, Ji Liu
For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios.
Ranked #1 on Camera shot boundary detection on ClipShots
no code implementations • 25 Mar 2021 • Wentao Zhu, Yufang Huang, Daguang Xu, Zhen Qian, Wei Fan, Xiaohui Xie
Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation.
no code implementations • 7 Jan 2021 • Yufang Huang, Kelly M. Axsom, John Lee, Lakshminarayanan Subramanian, Yiye Zhang
Following the representation learning and clustering steps, we embed the objective function in DICE with a constraint which requires a statistically significant association between the outcome and cluster membership of learned representations.
no code implementations • COLING 2020 • Yufang Huang, Wentao Zhu, Deyi Xiong, Yiye Zhang, Changjian Hu, Feiyu Xu
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation.
1 code implementation • 4 Oct 2019 • Wentao Zhu, Andriy Myronenko, Ziyue Xu, Wenqi Li, Holger Roth, Yufang Huang, Fausto Milletari, Daguang Xu
Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature.
no code implementations • 18 Jun 2019 • Wentao Zhu, Yufang Huang, Mani A. Vannan, Shizhen Liu, Daguang Xu, Wei Fan, Zhen Qian, Xiaohui Xie
In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking.
2 code implementations • 15 Aug 2018 • Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong liu, Zhen Qian, Nan Du, Wei Fan, Xiaohui Xie
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.
2 code implementations • 14 May 2018 • Wentao Zhu, Yeeleng S. Vang, Yufang Huang, Xiaohui Xie
Recently deep learning has been witnessing widespread adoption in various medical image applications.