Mahsa Shakeri
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Artificial Intelligence
- Neurology top 10%
- Biomedical Engineering
- Co-authors
- Samuel KadouryMichal DrozdzalLisa Di JorioGabriel ChartrandYoshua BengioAdriana RomeroChris PalEugene Vorontsov
- Topics
- Medical Image Segmentation Techniques (5 papers)Software System Performance and Reliability (3 papers)Image Retrieval and Classification Techniques (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessMedical Image Analysis
- Partner nations
- CanadaIranSwitzerland
In The Last Decade
Mahsa Shakeri
19 papers receiving 361 citations
Peers
Comparison fields: 5 of 83
- Computer Vision and Pattern Recognition 164
- Radiology, Nuclear Medicine and Imaging 134
- Artificial Intelligence 80
- Neurology 68
- Biomedical Engineering 65
Countries citing papers authored by Mahsa Shakeri
This map shows the geographic impact of Mahsa Shakeri'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 Mahsa Shakeri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mahsa Shakeri more than expected).
Fields of papers citing papers by Mahsa Shakeri
This network shows the impact of papers produced by Mahsa Shakeri. 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 Mahsa Shakeri. The network helps show where Mahsa Shakeri may publish in the future.
Co-authorship network of co-authors of Mahsa Shakeri
This figure shows the co-authorship network connecting the top 25 collaborators of Mahsa Shakeri. A scholar is included among the top collaborators of Mahsa Shakeri based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mahsa Shakeri. Mahsa Shakeri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 16 | |
| 4 | 9 | |
| 5 | 0 | |
| 6 | 3 | |
| 7 | 8 | |
| 8 | 6 | |
| 9 | 7 | |
| 10 | 3 | |
| 11 | 181 | |
| 12 | 18 | |
| 13 | 11 | |
| 14 | 8 | |
| 15 | 8 | |
| 16 | 7 | |
| 17 | 69 | |
| 18 | 3 | |
| 19 | 2 | |
| 20 | 4 |
About Mahsa Shakeri
Mahsa Shakeri is a scholar working on Computer Vision and Pattern Recognition, Geometry and Topology and Cancer Research, having authored 20 papers that have together received 365 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (5 papers), Software System Performance and Reliability (3 papers) and Image Retrieval and Classification Techniques (3 papers). The work is most often cited by research in Neurology (68 citations), Computer Vision and Pattern Recognition (164 citations) and Radiology, Nuclear Medicine and Imaging (134 citations). Mahsa Shakeri has collaborated with scholars based in Canada, Iran and Switzerland. Frequent co-authors include Samuel Kadoury, Michal Drozdzal, Lisa Di Jorio, Gabriel Chartrand, Yoshua Bengio, Adriana Romero, Chris Pal, Eugene Vorontsov, An Tang and Sarah Lippé. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access 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.