Andrew Lagree
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 5
- Medical Imaging Techniques and Applications 2
-
- AI in cancer detection 4
- Co-authors
- William T. Tran (13 shared papers)Ali Sadeghi‐Naini (10 shared papers)Sami Tabbarah (7 shared papers)Fang‐I Lu (8 shared papers)Jonathan Klein (4 shared papers)Sonal Gandhi (6 shared papers)Tina Wu (3 shared papers)Katarzyna J. Jerzak (4 shared papers)
- Journals
- JCO Clinical Cancer Informatics (2 papers)Scientific Reports (2 papers)Breast Cancer Research and Treatment (1 paper)Cancers (1 paper)International Journal of Radiation Oncology*Biology*Physics (1 paper)
- Partner nations
- CanadaUnited KingdomUnited States
In The Last Decade
Andrew Lagree
14 papers receiving 312 citations
Peers
Comparison fields: 5 of 59
- Health Informatics 23
- Radiology, Nuclear Medicine and Imaging 144
- Artificial Intelligence 129
- Cancer Research 39
- Biophysics 12
Countries citing papers authored by Andrew Lagree
This map shows the geographic impact of Andrew Lagree'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 Andrew Lagree with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Lagree more than expected).
Fields of papers citing papers by Andrew Lagree
This network shows the impact of papers produced by Andrew Lagree. 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 Andrew Lagree. The network helps show where Andrew Lagree may publish in the future.
Co-authors
The 25 scholars most cited alongside Andrew Lagree, 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 | 2019 | 65 | |
| 2 | 2021 | 51 | |
| 3 | 2021 | 31 | |
| 4 | 2020 | 30 | |
| 5 | 2021 | 27 | |
| 6 | 2022 | 27 | |
| 7 | 2023 | 20 | |
| 8 | 2019 | 18 | |
| 9 | 2022 | 13 | |
| 10 | 2021 | 13 | |
| 11 | 2023 | 11 | |
| 12 | 2023 | 4 | |
| 13 | 2024 | 3 | |
| 14 | 2020 | 2 | |
| 15 | 2024 | 0 |
About Andrew Lagree
Andrew Lagree is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Cancer Research, Oncology and Molecular Biology, having authored 15 papers that have together received 315 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), AI in cancer detection (4 papers), Breast Cancer Treatment Studies (3 papers), Medical Imaging Techniques and Applications (2 papers), Neutropenia and Cancer Infections (2 papers), Cancer Genomics and Diagnostics (1 paper), Digital Imaging for Blood Diseases (1 paper) and Brain Tumor Detection and Classification (1 paper). The work is most often cited by research in Health Informatics (23 citations), Radiology, Nuclear Medicine and Imaging (144 citations), Artificial Intelligence (129 citations), Cancer Research (39 citations) and Biophysics (12 citations). Andrew Lagree has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include William T. Tran, Ali Sadeghi‐Naini, Sami Tabbarah, Fang‐I Lu, Jonathan Klein, Sonal Gandhi, Tina Wu, Katarzyna J. Jerzak, Elzbieta Slodkowska and Eileen Rakovitch. Their work appears in journals such as JCO Clinical Cancer Informatics, Scientific Reports, Breast Cancer Research and Treatment, Cancers and International Journal of Radiation Oncology*Biology*Physics.
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.