Fábio J. Ayres
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Molecular Biology
- Ophthalmology top 5%
- Co-authors
- Rangaraj M. RangayyanJ. E. Leo DesautelsSteven K. BoydAlla BondarevaCharlene M. DowneyWei LiuBenedikt HallgrímssonFrank R. Jirik
- Topics
- AI in cancer detection (12 papers)Image Retrieval and Classification Techniques (10 papers)Medical Imaging Techniques and Applications (6 papers)
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingArtificial Intelligence
- Journals
- SHILAP Revista de lepidopterologíaApplied Physics LettersPLoS ONE
- Partner nations
- CanadaBrazilUnited States
In The Last Decade
Fábio J. Ayres
38 papers receiving 876 citations
Peers
Comparison fields: 5 of 98
- Computer Vision and Pattern Recognition 438
- Artificial Intelligence 429
- Radiology, Nuclear Medicine and Imaging 366
- Molecular Biology 186
- Ophthalmology 121
Countries citing papers authored by Fábio J. Ayres
This map shows the geographic impact of Fábio J. Ayres'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 Fábio J. Ayres with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fábio J. Ayres more than expected).
Fields of papers citing papers by Fábio J. Ayres
This network shows the impact of papers produced by Fábio J. Ayres. 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 Fábio J. Ayres. The network helps show where Fábio J. Ayres may publish in the future.
Co-authorship network of co-authors of Fábio J. Ayres
This figure shows the co-authorship network connecting the top 25 collaborators of Fábio J. Ayres. A scholar is included among the top collaborators of Fábio J. Ayres 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 Fábio J. Ayres. Fábio J. Ayres is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 26 | |
| 6 | 56 | |
| 7 | 29 | |
| 8 | 126 | |
| 9 | 4 | |
| 10 | 31 | |
| 11 | 1 | |
| 12 | 19 | |
| 13 | 67 | |
| 14 | 0 | |
| 15 | 2 | |
| 16 | 13 | |
| 17 | 2 | |
| 18 | 40 | |
| 19 | 17 | |
| 20 | 3 |
About Fábio J. Ayres
Fábio J. Ayres is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 42 papers that have together received 954 indexed citations. Recurring topics across this work include AI in cancer detection (12 papers), Image Retrieval and Classification Techniques (10 papers) and Medical Imaging Techniques and Applications (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (438 citations), Radiology, Nuclear Medicine and Imaging (366 citations) and Artificial Intelligence (429 citations). Fábio J. Ayres has collaborated with scholars based in Canada, Brazil and United States. Frequent co-authors include Rangaraj M. Rangayyan, J. E. Leo Desautels, Steven K. Boyd, Alla Bondareva, Charlene M. Downey, Wei Liu, Benedikt Hallgrímsson, Frank R. Jirik, Xiaolu Zhu and Anna L. Ells. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.
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.