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Yuyin Zhou
Researcher at Johns Hopkins University
Publications - 73
Citations - 4124
Yuyin Zhou is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 22, co-authored 63 publications receiving 2442 citations. Previous affiliations of Yuyin Zhou include Stanford University & University of Oxford.
Papers
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Book ChapterDOI
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
TL;DR: This chapter proposes a novel 3D-based coarse-to-fine framework that outperforms their 2D counterparts by a large margin and analyzes the threat of adversarial attacks on the proposed framework and shows how to defense against the attack.
Posted Content
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Yan Wang,Xu Wei,Fengze Liu,Jieneng Chen,Yuyin Zhou,Wei Shen,Elliot K. Fishman,Alan L. Yuille +7 more
TL;DR: This work proposes a geometry-aware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks, and applies it on six medical image datasets.
Book ChapterDOI
Multi-scale Attentional Network for Multi-focal Segmentation of Active Bleed After Pelvic Fractures
TL;DR: The Multi-Scale Attentional Network (MSAN), the first yet reliable end-to-end network, for automated segmentation of active hemorrhage from contrast-enhanced trauma CT scans, is presented.
Posted Content
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
TL;DR: In this article, the authors proposed a new sample selection policy, named Relaxed Upper Confident Bound (RUCB), which exploits a range of hard samples rather than being stuck with a small set of very hard ones, which mitigates the influence of annotation errors during training.
Book ChapterDOI
Pancreas CT Segmentation by Predictive Phenotyping
Yucheng Tang,Riqiang Gao,Ho Hin Lee,Qi Yang,Xin Yu,Yuyin Zhou,Shunxing Bao,Yuankai Huo,Jeffrey M. Spraggins,Jeffrey M. Spraggins,John Virostko,Zhoubing Xu,Bennett A. Landman,Bennett A. Landman +13 more
TL;DR: Wang et al. as discussed by the authors proposed the first phenotype embedding model for pancreas segmentation by predicting representatives that share similar comorbidities, which can adaptively refine segmentation outcome based on the discriminative contexts distilled from clinical features.