Gabriel Chartrand
- Health Informatics top 0.5%
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- Radiomics and Machine Learning in Medical Imaging 7
- Medical Imaging Techniques and Applications 2
- Hepatology top 5%
- Hepatocellular Carcinoma Treatment and Prognosis 4
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- Medical Image Segmentation Techniques 6
- Advanced Neural Network Applications 4
- Artificial Intelligence top 5%
- AI in cancer detection 4
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- Liver Disease Diagnosis and Treatment 3
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- Medical Imaging and Analysis 2
- Co-authors
- An TangSamuel KadouryMichal DrozdzalEugene VorontsovPhillip M. ChengChristopher PalSimon TurcotteJacques A. de Guise
- Journals
- Radiographics (3 papers)Journal of Magnetic Resonance Imaging (2 papers)IEEE Transactions on Biomedical Engineering (1 paper)
- Partner nations
- CanadaUnited StatesPoland
In The Last Decade
Gabriel Chartrand
18 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Health Informatics 184
- Radiology, Nuclear Medicine and Imaging 823
- Hepatology 160
- Computer Vision and Pattern Recognition 268
- Artificial Intelligence 379
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-authorship network
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 | 2025 | 0 | |
| 2 | 2022 | 14 | |
| 3 | 2021 | 105 | |
| 4 | Learning to Learn with Conditional Class Dependencies | 2018 | 27 |
| 5 | Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification | 2018 | 15 |
| 6 | 2017 | 134 | |
| 7 | Deep Learning: A Primer for Radiologistsbreakdown → | 2017 | 788 |
| 8 | 2017 | 181 | |
| 9 | 2017 | 1 | |
| 10 | 2016 | 3 | |
| 11 | 2016 | 20 | |
| 12 | 2016 | 53 | |
| 13 | 2015 | 20 | |
| 14 | 2015 | 16 | |
| 15 | 2015 | 1 | |
| 16 | 2015 | 107 | |
| 17 | 2015 | 43 | |
| 18 | 2014 | 6 | |
| 19 | 2014 | 18 |
About Gabriel Chartrand
Gabriel Chartrand is a scholar working on Equine, Hepatology and Radiology, Nuclear Medicine and Imaging, having authored 19 papers that have together received 1.6k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), Medical Image Segmentation Techniques (6 papers), Hepatocellular Carcinoma Treatment and Prognosis (4 papers), AI in cancer detection (4 papers), Advanced Neural Network Applications (4 papers), Liver Disease Diagnosis and Treatment (3 papers), Medical Imaging Techniques and Applications (2 papers) and Medical Imaging and Analysis (2 papers). The work is most often cited by research in Health Informatics (184 citations), Radiology, Nuclear Medicine and Imaging (823 citations) and Hepatology (160 citations). Gabriel Chartrand has collaborated with scholars based in Canada, United States and Poland. Frequent co-authors include An Tang, Samuel Kadoury, Michal Drozdzal, Eugene Vorontsov, Phillip M. Cheng, Christopher Pal, Simon Turcotte, Jacques A. de Guise, Akshat Gotra and Kim‐Nhien Vu. Their work appears in journals such as Radiographics, Journal of Magnetic Resonance Imaging, IEEE Transactions on Biomedical Engineering, Insights into Imaging and Academic Radiology.
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.