Gabriel Chartrand
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
-
- Medical Image Segmentation Techniques 6
- Advanced Neural Network Applications 3
-
- AI in cancer detection 4
- Topic Modeling 2
- Co-authors
- An Tang (12 shared papers)Samuel Kadoury (4 shared papers)Eugene Vorontsov (2 shared papers)Michal Drozdzal (2 shared papers)Phillip M. Cheng (3 shared papers)Simon Turcotte (1 shared paper)Christopher Pal (1 shared paper)Jacques A. de Guise (10 shared papers)
- Journals
- Radiographics (3 papers)Journal of Magnetic Resonance Imaging (2 papers)Diabetes Care (1 paper)Abdominal Radiology (1 paper)Medical Image Analysis (1 paper)
- Partner nations
- CanadaUnited StatesPoland
In The Last Decade
Gabriel Chartrand
19 papers receiving 1.6k citations
Gabriel Chartrand's Hit Papers
Peers
Comparison fields: 5 of 127
- Health Informatics 157
- Radiology, Nuclear Medicine and Imaging 613
- Hepatology 110
- Computer Vision and Pattern Recognition 247
- Neurology 79
Countries citing papers authored by Gabriel Chartrand
This map shows the geographic impact of Gabriel Chartrand'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 Gabriel Chartrand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriel Chartrand more than expected).
Fields of papers citing papers by Gabriel Chartrand
This network shows the impact of papers produced by Gabriel Chartrand. 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 Gabriel Chartrand. The network helps show where Gabriel Chartrand may publish in the future.
Co-authors
The 25 scholars most cited alongside Gabriel Chartrand, 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: A Primer for Radiologists Hit paper breakdown → | 2017 | 811 |
| 2 | 2017 | 181 | |
| 3 | 2017 | 137 | |
| 4 | 2021 | 112 | |
| 5 | 2015 | 110 | |
| 6 | 2016 | 53 | |
| 7 | 2015 | 43 | |
| 8 | Learning to Learn with Conditional Class Dependencies | 2018 | 27 |
| 9 | 2015 | 21 | |
| 10 | 2016 | 20 | |
| 11 | 2014 | 18 | |
| 12 | 2015 | 17 | |
| 13 | 2022 | 15 | |
| 14 | Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification | 2018 | 15 |
| 15 | 2014 | 6 | |
| 16 | 2016 | 3 | |
| 17 | 2025 | 1 | |
| 18 | 2017 | 1 | |
| 19 | 2015 | 1 |
About Gabriel Chartrand
Gabriel Chartrand is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Epidemiology and Biomedical Engineering, having authored 19 papers that have together received 1.6k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), AI in cancer detection (4 papers), Advanced Neural Network Applications (3 papers), Medical Imaging and Analysis (2 papers), Liver Disease Diagnosis and Treatment (2 papers), Topic Modeling (2 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Health Informatics (157 citations), Radiology, Nuclear Medicine and Imaging (613 citations), Hepatology (110 citations), Computer Vision and Pattern Recognition (247 citations) and Neurology (79 citations). Gabriel Chartrand has collaborated with scholars based in Canada, United States and Poland. Frequent co-authors include An Tang, Samuel Kadoury, Eugene Vorontsov, Michal Drozdzal, Phillip M. Cheng, Simon Turcotte, Christopher Pal, Jacques A. de Guise, Akshat Gotra and Kim‐Nhien Vu. Their work appears in journals such as Radiographics, Journal of Magnetic Resonance Imaging, Diabetes Care, Abdominal Radiology and Medical Image Analysis.
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.