Lisa Di Jorio
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- COVID-19 diagnosis using AI 2
- Radiomics and Machine Learning in Medical Imaging 1
- Medical Imaging Techniques and Applications 1
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- Multimodal Machine Learning Applications 2
- Generative Adversarial Networks and Image Synthesis 1
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- Data Mining Algorithms and Applications 3
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- Natural Language Processing Techniques 1
- Machine Learning in Healthcare 1
- Co-authors
- Samuel KadouryEugene VorontsovAn TangYoshua BengioMichal DrozdzalGabriel ChartrandAdriana RomeroMahsa Shakeri
- Cited by
- Radiology, Nuclear Medicine and ImagingHealth InformaticsComputer Vision and Pattern Recognition
- Journals
- Medical Image Analysis (1 paper)Radiology Artificial Intelligence (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (4 papers)
In The Last Decade
Lisa Di Jorio
8 papers receiving 308 citations
Peers
Comparison fields: 5 of 60
- Radiology, Nuclear Medicine and Imaging 163
- Health Informatics 9
- Computer Vision and Pattern Recognition 133
- Neurology 43
- Hepatology 26
Countries citing papers authored by Lisa Di Jorio
This map shows the geographic impact of Lisa Di Jorio'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 Lisa Di Jorio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lisa Di Jorio more than expected).
Fields of papers citing papers by Lisa Di Jorio
This network shows the impact of papers produced by Lisa Di Jorio. 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 Lisa Di Jorio. The network helps show where Lisa Di Jorio may publish in the future.
Co-authorship network
The 24 scholars most cited alongside Lisa Di Jorio, 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 | 2021 | 4 | |
| 2 | 2019 | 95 | |
| 3 | 2019 | 19 | |
| 4 | Iteratively unveiling new regions of interest in Deep Learning models | 2018 | 1 |
| 5 | 2017 | 181 | |
| 6 | Paper Recommendation System: A Global and Soft Approach | 2012 | 3 |
| 7 | Mining for Gradualness Over Time Using Sequential Patterns | 2009 | 1 |
| 8 | 2008 | 14 | |
| 9 | Enrichissement d'ontologie basé sur les motifs séquentiels | 2007 | 1 |
About Lisa Di Jorio
Lisa Di Jorio is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 9 papers that have together received 319 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (3 papers), Multimodal Machine Learning Applications (2 papers), COVID-19 diagnosis using AI (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Natural Language Processing Techniques (1 paper), Medical Imaging Techniques and Applications (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (163 citations), Health Informatics (9 citations) and Computer Vision and Pattern Recognition (133 citations). Lisa Di Jorio has collaborated with scholars based in Canada, France and Algeria. Frequent co-authors include Samuel Kadoury, Eugene Vorontsov, An Tang, Yoshua Bengio, Michal Drozdzal, Gabriel Chartrand, Adriana Romero, Mahsa Shakeri, Chris Pal and Réal Lapointe. Their work appears in journals such as Medical Image Analysis, Radiology Artificial Intelligence and HAL (Le Centre pour la Communication Scientifique Directe).
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.