Z
Zichen Zhang
Researcher at University of Alberta
Publications - 37
Citations - 1297
Zichen Zhang is an academic researcher from University of Alberta. The author has contributed to research in topics: Biology & Medicine. The author has an hindex of 9, co-authored 27 publications receiving 578 citations. Previous affiliations of Zichen Zhang include Dalhousie University & Huazhong University of Science and Technology.
Papers
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Proceedings ArticleDOI
BASNet: Boundary-Aware Salient Object Detection
TL;DR: Experimental results on six public datasets show that the proposed predict-refine architecture, BASNet, outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures.
Posted Content
2017 Robotic Instrument Segmentation Challenge.
Max Allan,Alexey A. Shvets,Thomas Kurmann,Zichen Zhang,Rahul Duggal,Yun-Hsuan Su,Nicola Rieke,Iro Laina,Niveditha Kalavakonda,Sebastian Bodenstedt,Luis C. García-Peraza,Wenqi Li,Vladimir Iglovikov,Huoling Luo,Jian Yang,Danail Stoyanov,Lena Maier-Hein,Stefanie Speidel,Mahdi Azizian +18 more
TL;DR: The results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments are presented.
Proceedings ArticleDOI
ByLabel: A Boundary Based Semi-Automatic Image Annotation Tool
TL;DR: This paper presents a novel boundary based semiautomatic tool, ByLabel, for accurate image annotation, which reduces image-clicks and time by 73% and 56% respectively, while improving the accuracy by 73%.
Proceedings ArticleDOI
Online Object and Task Learning via Human Robot Interaction
TL;DR: The implementation and integration of the main modules of the system are described and the lessons learned from the competition are summarized.
Proceedings ArticleDOI
Segmentation-by-detection: A cascade network for volumetric medical image segmentation
TL;DR: In this article, an attention mechanism for 3D medical image segmentation is proposed, where a detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model.