Mikhail Milchenko
- Health Informatics top 0.5%
-
- Advanced Neuroimaging Techniques and Applications 8
- Advanced MRI Techniques and Applications 7
- Radiomics and Machine Learning in Medical Imaging 5
- MRI in cancer diagnosis 4
- Medical Imaging Techniques and Applications 4
- Artificial Intelligence top 5%
- Neurology top 10%
- Cognitive Neuroscience top 10%
- Functional Brain Connectivity Studies 5
-
- Glioma Diagnosis and Treatment 4
-
- Medical Image Segmentation Techniques 3
- Co-authors
- Daniel S. MarcusMicah ShellerWeilin XuG. Anthony ReinaDaniel C. MarcusSpyridon BakasSarthak PatiBrandon Edwards
- Partner nations
- United StatesNetherlandsLebanon
In The Last Decade
Mikhail Milchenko
22 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 105
- Health Informatics 153
- Radiology, Nuclear Medicine and Imaging 437
- Artificial Intelligence 467
- Neurology 90
- Cognitive Neuroscience 193
Countries citing papers authored by Mikhail Milchenko
This map shows the geographic impact of Mikhail Milchenko'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 Mikhail Milchenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mikhail Milchenko more than expected).
Fields of papers citing papers by Mikhail Milchenko
This network shows the impact of papers produced by Mikhail Milchenko. 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 Mikhail Milchenko. The network helps show where Mikhail Milchenko may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mikhail Milchenko, 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 | 2023 | 3 | |
| 2 | 2021 | 1 | |
| 3 | 2021 | 11 | |
| 4 | 2021 | 41 | |
| 5 | 2020 | 4 | |
| 6 | 2020 | 35 | |
| 7 | Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient databreakdown → | 2020 | 701 |
| 8 | 2020 | 27 | |
| 9 | 2020 | 1 | |
| 10 | 2019 | 1 | |
| 11 | 2018 | 45 | |
| 12 | 2018 | 6 | |
| 13 | 2017 | 48 | |
| 14 | 2016 | 3 | |
| 15 | 2015 | 5 | |
| 16 | 2014 | 7 | |
| 17 | 2013 | 6 | |
| 18 | 2013 | 10 | |
| 19 | 2012 | 95 | |
| 20 | 2006 | 6 |
About Mikhail Milchenko
Mikhail Milchenko is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Neurology, Genetics and Cognitive Neuroscience, having authored 23 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (8 papers), Advanced MRI Techniques and Applications (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Functional Brain Connectivity Studies (5 papers), MRI in cancer diagnosis (4 papers), Medical Imaging Techniques and Applications (4 papers), Glioma Diagnosis and Treatment (4 papers) and Medical Image Segmentation Techniques (3 papers). The work is most often cited by research in Health Informatics (153 citations), Radiology, Nuclear Medicine and Imaging (437 citations), Artificial Intelligence (467 citations), Neurology (90 citations) and Cognitive Neuroscience (193 citations). Mikhail Milchenko has collaborated with scholars based in United States, Netherlands and Lebanon. Frequent co-authors include Daniel S. Marcus, Micah Sheller, Weilin Xu, G. Anthony Reina, Daniel C. Marcus, Spyridon Bakas, Sarthak Pati, Brandon Edwards, Rivka R. Colen and Aikaterini Kotrotsou. Their work appears in journals such as PLoS ONE, Neuroinformatics, Radiology Artificial Intelligence, Scientific Reports and Neurosurgery.
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.