Y
Yee-Wah Tsang
Researcher at Coventry Health Care
Publications - 4
Citations - 159
Yee-Wah Tsang is an academic researcher from Coventry Health Care. The author has contributed to research in topics: Persistent homology & Deep learning. The author has an hindex of 3, co-authored 4 publications receiving 107 citations. Previous affiliations of Yee-Wah Tsang include University of Warwick.
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Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
Talha Qaiser,Yee-Wah Tsang,Daiki Taniyama,Naoya Sakamoto,Kazuaki Nakane,David Epstein,Nasir M. Rajpoot,Nasir M. Rajpoot,Nasir M. Rajpoot +8 more
TL;DR: In this paper, the authors proposed a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs), which can distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei.
Posted Content
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features
Talha Qaiser,Yee-Wah Tsang,Daiki Taniyama,Naoya Sakamoto,Kazuaki Nakane,David Epstein,Nasir M. Rajpoot,Nasir M. Rajpoot,Nasir M. Rajpoot +8 more
TL;DR: Wang et al. as discussed by the authors proposed a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs), which can distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei.
Journal ArticleDOI
Simultaneous automatic scoring and co‐registration of hormone receptors in tumor areas in whole slide images of breast cancer tissue slides
Nicholas Trahearn,Yee-Wah Tsang,Ian A. Cree,David Snead,David B. A. Epstein,Nasir M. Rajpoot +5 more
TL;DR: In this paper, an automated method for co-localized scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images was presented.
Posted Content
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
Simon Graham,Mostafa Jahanifar,Ayesha Azam,Mohammed Nimir,Yee-Wah Tsang,Katherine Dodd,Emily Hero,Harvir Sahota,Atisha Tank,Ksenija Benes,Noorul Wahab,Fayyaz ul Amir Afsar Minhas,Shan E Ahmed Raza,Hesham El Daly,Kishore Gopalakrishnan,David Snead,Nasir M. Rajpoot +16 more
TL;DR: In this article, a multi-stage annotation pipeline is proposed to enable the collection of large-scale datasets for histology image analysis, with pathologist-in-the-loop refinement steps.