Yee‐Wah Tsang

5.0k total citations · 1 hit paper
6 papers, 1.2k citations indexed

About

Yee‐Wah Tsang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biophysics. According to data from OpenAlex, Yee‐Wah Tsang has authored 6 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Biophysics. Recurrent topics in Yee‐Wah Tsang's work include AI in cancer detection (3 papers), Cell Image Analysis Techniques (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Yee‐Wah Tsang is often cited by papers focused on AI in cancer detection (3 papers), Cell Image Analysis Techniques (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Yee‐Wah Tsang collaborates with scholars based in United Kingdom, Australia and Japan. Yee‐Wah Tsang's co-authors include Nasir Rajpoot, David Snead, Ian A. Cree, Korsuk Sirinukunwattana, Shan E Ahmed Raza, Muhammad Moazam Fraz, Katherine Hewitt, Navid Alemi Koohbanani, Sajid Javed and Arif Mahmood and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Medical Imaging and Medical Image Analysis.

In The Last Decade

Yee‐Wah Tsang

5 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

2016 826 citations Korsuk Sirinukunwattana, Shan E Ahmed Raza et al. IEEE Transactions on Medical Imaging profile →

Peers

Yee‐Wah Tsang

AI

RNMI

CVPR

BIOPH

ONCOL

Simon Graham United Kingdom

Abhishek Vahadane India

Dimitris Samaras United States

Shadi Albarqouni Germany

Meyke Hermsen Netherlands

N. K. Timofeeva Netherlands

Yuanpu Xie United States

Ruchika Verma United States

Ángel Cruz-Roa Colombia

Olcay Sertel United States

Simon Graham United Kingdom

Yee‐Wah Tsang

770 ×0.9

AI

590 ×1.1

RNMI

389 ×0.9

CVPR

154 ×0.7

BIOPH

183 ×1.0

ONCOL

Citations per year, relative to Yee‐Wah Tsang Yee‐Wah Tsang (= 1×) peers Simon Graham

Countries citing papers authored by Yee‐Wah Tsang

Since Specialization

Citations

This map shows the geographic impact of Yee‐Wah Tsang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yee‐Wah Tsang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yee‐Wah Tsang more than expected).

Fields of papers citing papers by Yee‐Wah Tsang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yee‐Wah Tsang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yee‐Wah Tsang. The network helps show where Yee‐Wah Tsang may publish in the future.

Co-authorship network of co-authors of Yee‐Wah Tsang

This figure shows the co-authorship network connecting the top 25 collaborators of Yee‐Wah Tsang. A scholar is included among the top collaborators of Yee‐Wah Tsang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yee‐Wah Tsang. Yee‐Wah Tsang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Lee, Grace, Tara Rosewall, Xiang Y. Ye, et al.. (2024). Unveiling Patient and Provider Perceptions on Medical Tattoos in Breast Cancer Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 120(2). e317–e318.

2.

Javed, Sajid, Arif Mahmood, Muhammad Moazam Fraz, et al.. (2020). Cellular community detection for tissue phenotyping in colorectal cancer histology images. Medical Image Analysis. 63. 101696–101696. 101 indexed citations

3.

Qaiser, Talha, et al.. (2016). Persistent Homology for Fast Tumor Segmentation in Whole Slide Histology Images. Procedia Computer Science. 90. 119–124. 39 indexed citations

4.

Sirinukunwattana, Korsuk, Shan E Ahmed Raza, Yee‐Wah Tsang, et al.. (2016). Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Transactions on Medical Imaging. 35(5). 1196–1206. 826 indexed citations breakdown →

5.

Snead, David, Yee‐Wah Tsang, Peter Kimani, et al.. (2015). Validation of digital pathology imaging for primary histopathological diagnosis. Histopathology. 68(7). 1063–1072. 168 indexed citations

6.

Türk, Elisabeth E., et al.. (2010). Cardiac injuries in car occupants in fatal motor vehicle collisions – An autopsy-based study. Journal of Forensic and Legal Medicine. 17(6). 339–343. 28 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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