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
- COVID-19 diagnosis using AI 1
- MRI in cancer diagnosis 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)Mikhail Milchenko (1 shared paper)Rivka R. Colen (1 shared paper)Weilin Xu (1 shared paper)
- Journals
- Neuro-Oncology (2 papers)Physics in Medicine and Biology (2 papers)Scientific Reports (1 paper)Patterns (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Micah Sheller
7 papers receiving 1.1k citations
Micah Sheller's Hit Papers
Peers
Comparison fields: 5 of 104
- Health Informatics 215
- Artificial Intelligence 749
- Radiology, Nuclear Medicine and Imaging 338
- Health Information Management 45
- Neurology 73
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 | 701 |
| 2 | 2019 | 282 | |
| 3 | 2022 | 56 | |
| 4 | 2024 | 38 | |
| 5 | 2022 | 17 | |
| 6 | 2021 | 4 | |
| 7 | 2019 | 3 |
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 1.1k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (6 papers), Privacy-Preserving Technologies in Data (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Cancer Genomics and Diagnostics (2 papers), Glioma Diagnosis and Treatment (2 papers), COVID-19 diagnosis using AI (1 paper), Cryptography and Data Security (1 paper) and MRI in cancer diagnosis (1 paper). The work is most often cited by research in Health Informatics (215 citations), Artificial Intelligence (749 citations), Radiology, Nuclear Medicine and Imaging (338 citations), Health Information Management (45 citations) and Neurology (73 citations). Micah Sheller has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Brandon Edwards, Spyridon Bakas, Jason Martin, G. Anthony Reina, Sarthak Pati, Mikhail Milchenko, Rivka R. Colen, Weilin Xu, Aikaterini Kotrotsou and Daniel C. Marcus. Their work appears in journals such as Neuro-Oncology, Physics in Medicine and Biology, Scientific Reports, Patterns 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.