Yee‐Wah Tsang
- Artificial Intelligence top 1%
- Radiology, Nuclear Medicine and Imaging top 2%
- Computer Vision and Pattern Recognition top 2%
- Biophysics top 1%
- Oncology
- Co-authors
- Nasir RajpootDavid SneadIan A. CreeKorsuk SirinukunwattanaShan E Ahmed RazaMuhammad Moazam FrazKatherine HewittNavid Alemi Koohbanani
- Topics
- AI in cancer detection (3 papers)Cell Image Analysis Techniques (2 papers)Radiomics and Machine Learning in Medical Imaging (1 paper)
- Journals
- International Journal of Radiation Oncology*Biology*PhysicsIEEE Transactions on Medical ImagingMedical Image Analysis
- Partner nations
- United KingdomAustraliaJapan
In The Last Decade
Yee‐Wah Tsang
5 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Artificial Intelligence 857
- Radiology, Nuclear Medicine and Imaging 551
- Computer Vision and Pattern Recognition 429
- Biophysics 224
- Oncology 189
Countries citing papers authored by Yee‐Wah Tsang
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 101 | |
| 3 | 39 | |
| 4 | Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Imagesbreakdown → | 826 |
| 5 | 168 | |
| 6 | 28 |
About Yee‐Wah Tsang
Yee‐Wah Tsang is a scholar working on Biophysics, Cultural Studies and Emergency Medicine, having authored 6 papers that have together received 1.2k indexed citations. Recurring topics across this work include AI in cancer detection (3 papers), Cell Image Analysis Techniques (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). The work is most often cited by research in Biophysics (224 citations), Health Informatics (43 citations) and Artificial Intelligence (857 citations). Yee‐Wah Tsang has collaborated with scholars based in United Kingdom, Australia and Japan. Frequent 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. Their work appears in journals such as International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Medical Imaging and Medical Image Analysis.
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.