M
Min Zhang
Researcher at Brigham and Women's Hospital
Publications - 31
Citations - 398
Min Zhang is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Ricci flow & Curvature. The author has an hindex of 9, co-authored 31 publications receiving 234 citations. Previous affiliations of Min Zhang include Stony Brook University & Zhejiang University.
Papers
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Journal ArticleDOI
Deep-Learning Detection of Cancer Metastases to the Brain on MRI
Min Zhang,Geoffrey S. Young,Huai Chen,Huai Chen,Jing Li,Jing Li,Lei Qin,J Ricardo McFaline-Figueroa,David A. Reardon,Xinhua Cao,Xian Wu,Xiaoyin Xu +11 more
TL;DR: Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective.
Journal ArticleDOI
Area-preserving mesh parameterization for poly-annulus surfaces based on optimal mass transportation
TL;DR: This work proposes a novel method for computing area-preserving parameterization for genus zero surfaces with multiple boundaries (poly-annuli) based on discrete optimal mass transportation and surface Ricci Flow, and the resulting parameterization preserves area element and minimizes angle distortion.
Journal ArticleDOI
The unified discrete surface Ricci flow
TL;DR: Experimental results show the unified surface Ricci flow algorithms can handle general surfaces with different topologies, and are robust to meshes with different qualities, and is effective for solving real problems.
Proceedings ArticleDOI
Generalized Koebe's method for conformal mapping multiply connected domains
TL;DR: This work generalizes conventional Koebe's method for multiply connected planar domains with theoretic proof and estimation for the converging rate, and introduces a practical algorithm to explicitly construct such a circular conformal mapping.
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Spherical optimal transportation
TL;DR: This work introduces a novel theoretic framework and computational algorithm to compute the optimal transportation map on the sphere using the variational framework and Newton’s method and demonstrates efficacy and efficiency of the proposed method on a variety of models.