Micah Sheller
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data
- AI in cancer detection
- Cryptography and Data Security
- Machine Learning in Healthcare
Papers in
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- Artificial Intelligence in Healthcare and Education 6
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- Radiomics and Machine Learning in Medical Imaging 4
- MRI in cancer diagnosis 1
- COVID-19 diagnosis using AI 1
- Co-authors
- Brandon Edwards (7 shared papers)Spyridon Bakas (7 shared papers)Jason Martin (6 shared papers)G. Anthony Reina (4 shared papers)Sarthak Pati (5 shared papers)Weilin Xu (1 shared paper)Mikhail Milchenko (1 shared paper)Rivka R. Colen (1 shared paper)
- Journals
- Physics in Medicine and Biology (2 papers)Neuro-Oncology (2 papers)Patterns (1 paper)Scientific Reports (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- United StatesUnited KingdomHong Kong
In The Last Decade
Micah Sheller
7 papers receiving 1.3k citations
Micah Sheller's Hit Papers
Peers
Comparison fields: 5 of 101
- Health Informatics 216
- Artificial Intelligence 846
- Radiology, Nuclear Medicine and Imaging 361
- Neurology 88
- Health Information Management 42
Countries citing papers authored by Micah Sheller
This map shows the geographic impact of Micah Sheller'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 Micah Sheller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Micah Sheller more than expected).
Fields of papers citing papers by Micah Sheller
This network shows the impact of papers produced by Micah Sheller. 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 Micah Sheller. The network helps show where Micah Sheller may publish in the future.
Co-authors
The 24 scholars most cited alongside Micah Sheller, 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 | Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data Hit paper breakdown → | 2020 | 854 |
| 2 | 2019 | 341 | |
| 3 | 2022 | 70 | |
| 4 | 2024 | 61 | |
| 5 | 2022 | 20 | |
| 6 | 2019 | 6 | |
| 7 | 2021 | 4 |
About Micah Sheller
Micah Sheller is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Genetics and Cancer Research, having authored 7 papers that have together received 1.4k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Privacy-Preserving Technologies in Data (4 papers), Cancer Genomics and Diagnostics (2 papers), Glioma Diagnosis and Treatment (2 papers), MRI in cancer diagnosis (1 paper), Cryptography and Data Security (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (216 citations), Artificial Intelligence (846 citations), Radiology, Nuclear Medicine and Imaging (361 citations), Neurology (88 citations) and Health Information Management (42 citations). Micah Sheller has collaborated with scholars based in United States, United Kingdom and Hong Kong. Frequent co-authors include Brandon Edwards, Spyridon Bakas, Jason Martin, G. Anthony Reina, Sarthak Pati, Weilin Xu, Mikhail Milchenko, Rivka R. Colen, Aikaterini Kotrotsou and Daniel C. Marcus. Their work appears in journals such as Physics in Medicine and Biology, Neuro-Oncology, Patterns, Scientific Reports and Lecture notes in computer science.
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.