Mane Williams
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- AI in cancer detection 4
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- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Faisal Mahmood (5 shared papers)Ming Y. Lu (5 shared papers)Richard J. Chen (4 shared papers)Drew F. K. Williamson (4 shared papers)Muhammad Shaban (3 shared papers)Maha Shady (4 shared papers)Tiffany Chen (4 shared papers)Jana Lipková (4 shared papers)
- Journals
- Nature Medicine (2 papers)Journal of Pathology Informatics (1 paper)Cancer Cell (1 paper)Cell (1 paper)Journal of the American College of Cardiology (1 paper)
- Partner nations
- United StatesSwitzerlandTürkiye
In The Last Decade
Mane Williams
6 papers receiving 852 citations
Mane Williams's Hit Papers
Peers
Comparison fields: 5 of 71
- Health Informatics 83
- Radiology, Nuclear Medicine and Imaging 295
- Biophysics 80
- Artificial Intelligence 446
- Cancer Research 93
Countries citing papers authored by Mane Williams
This map shows the geographic impact of Mane Williams'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 Mane Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mane Williams more than expected).
Fields of papers citing papers by Mane Williams
This network shows the impact of papers produced by Mane Williams. 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 Mane Williams. The network helps show where Mane Williams may publish in the future.
Co-authors
The 25 scholars most cited alongside Mane Williams, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Towards a general-purpose foundation model for computational pathology Hit paper breakdown → | 2024 | 437 |
| 2 | Pan-cancer integrative histology-genomic analysis via multimodal deep learning Hit paper breakdown → | 2022 | 325 |
| 3 | 2022 | 54 | |
| 4 | 2024 | 40 | |
| 5 | 2022 | 5 | |
| 6 | 2021 | 1 |
About Mane Williams
Mane Williams is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Surgery, Molecular Biology and Cancer Research, having authored 6 papers that have together received 862 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Cancer Genomics and Diagnostics (2 papers), Transplantation: Methods and Outcomes (2 papers), Viral Infections and Immunology Research (1 paper), Biomedical Text Mining and Ontologies (1 paper), Fuel Cells and Related Materials (1 paper) and Mechanical Circulatory Support Devices (1 paper). The work is most often cited by research in Health Informatics (83 citations), Radiology, Nuclear Medicine and Imaging (295 citations), Biophysics (80 citations), Artificial Intelligence (446 citations) and Cancer Research (93 citations). Mane Williams has collaborated with scholars based in United States, Switzerland and Türkiye. Frequent co-authors include Faisal Mahmood, Ming Y. Lu, Richard J. Chen, Drew F. K. Williamson, Muhammad Shaban, Maha Shady, Tiffany Chen, Jana Lipková, Guillaume Jaume and Bowen Chen. Their work appears in journals such as Nature Medicine, Journal of Pathology Informatics, Cancer Cell, Cell and Journal of the American College of Cardiology.
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.