Michelle Bardis
Impact in
- Health Informatics top 5%
- Genetics top 5%
- Glioma Diagnosis and Treatment
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 8
- Radiation Dose and Imaging 1
-
- Prostate Cancer Diagnosis and Treatment 5
- Renal cell carcinoma treatment 2
- Co-authors
- Daniel Chow (12 shared papers)Christopher G. Filippi (3 shared papers)Peter Chang (3 shared papers)Brent D. Weinberg (3 shared papers)Jack Grinband (2 shared papers)Daniela A. Bota (2 shared papers)Laila Poisson (1 shared paper)Pierre Baldi (1 shared paper)
- Journals
- Journal of Clinical Oncology (3 papers)Cancers (2 papers)American Journal of Neuroradiology (2 papers)Journal of Endourology (1 paper)Electronics (1 paper)
- Partner nations
- United StatesTaiwanIndia
In The Last Decade
Michelle Bardis
12 papers receiving 561 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Health Informatics 43
- Genetics 178
- Radiology, Nuclear Medicine and Imaging 370
- Neurology 104
- Pulmonary and Respiratory Medicine 151
Countries citing papers authored by Michelle Bardis
This map shows the geographic impact of Michelle Bardis'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 Michelle Bardis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michelle Bardis more than expected).
Fields of papers citing papers by Michelle Bardis
This network shows the impact of papers produced by Michelle Bardis. 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 Michelle Bardis. The network helps show where Michelle Bardis may publish in the future.
Co-authors
The 24 scholars most cited alongside Michelle Bardis, 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 | Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas Hit paper breakdown → | 2018 | 325 |
| 2 | 2019 | 67 | |
| 3 | 2020 | 47 | |
| 4 | 2020 | 43 | |
| 5 | 2021 | 42 | |
| 6 | 2020 | 25 | |
| 7 | 2021 | 11 | |
| 8 | 2019 | 9 | |
| 9 | 2020 | 2 | |
| 10 | 2019 | 2 | |
| 11 | 2020 | 2 | |
| 12 | 2019 | 1 |
About Michelle Bardis
Michelle Bardis is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Biomedical Engineering, Genetics and Rheumatology, having authored 12 papers that have together received 576 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (8 papers), Prostate Cancer Diagnosis and Treatment (5 papers), Medical Imaging and Analysis (3 papers), Urologic and reproductive health conditions (2 papers), Advanced X-ray and CT Imaging (2 papers), Glioma Diagnosis and Treatment (2 papers), Renal cell carcinoma treatment (2 papers) and Radiation Dose and Imaging (1 paper). The work is most often cited by research in Health Informatics (43 citations), Genetics (178 citations), Radiology, Nuclear Medicine and Imaging (370 citations), Neurology (104 citations) and Pulmonary and Respiratory Medicine (151 citations). Michelle Bardis has collaborated with scholars based in United States, Taiwan and India. Frequent co-authors include Daniel Chow, Christopher G. Filippi, Peter Chang, Brent D. Weinberg, Jack Grinband, Daniela A. Bota, Laila Poisson, Pierre Baldi, Min‐Ying Su and Soonmee Cha. Their work appears in journals such as Journal of Clinical Oncology, Cancers, American Journal of Neuroradiology, Journal of Endourology and Electronics.
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.