Guilherme Aresta
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- Retinal Imaging and Analysis
- COVID-19 diagnosis using AI
- Health Informatics top 2%
Papers in
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- Retinal Imaging and Analysis 19
- Radiomics and Machine Learning in Medical Imaging 14
- COVID-19 diagnosis using AI 8
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- Retinal Diseases and Treatments 7
- Co-authors
- Aurélio Campilho (18 shared papers)Teresa Araújo (16 shared papers)Catarina Eloy (3 shared papers)Paulo Aguiar (3 shared papers)António Polónia (3 shared papers)José Rouco (2 shared papers)Eduardo Castro (1 shared paper)Ana Maria Mendonça (8 shared papers)
In The Last Decade
Guilherme Aresta
36 papers receiving 1.9k citations
Guilherme Aresta's Hit Papers
Peers
Comparison fields: 5 of 110
- Radiology, Nuclear Medicine and Imaging 1.2k
- Health Informatics 65
- Artificial Intelligence 1.3k
- Computer Vision and Pattern Recognition 721
- Biophysics 127
Countries citing papers authored by Guilherme Aresta
This map shows the geographic impact of Guilherme Aresta'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 Guilherme Aresta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guilherme Aresta more than expected).
Fields of papers citing papers by Guilherme Aresta
This network shows the impact of papers produced by Guilherme Aresta. 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 Guilherme Aresta. The network helps show where Guilherme Aresta may publish in the future.
Co-authors
The 25 scholars most cited alongside Guilherme Aresta, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Classification of breast cancer histology images using Convolutional Neural Networks Hit paper breakdown → | 2017 | 789 |
| 2 | BACH: Grand challenge on breast cancer histology images Hit paper breakdown → | 2019 | 522 |
| 3 | 2020 | 102 | |
| 4 | 2018 | 91 | |
| 5 | 2020 | 51 | |
| 6 | 2023 | 48 | |
| 7 | 2022 | 39 | |
| 8 | 2018 | 31 | |
| 9 | 2019 | 28 | |
| 10 | 2020 | 26 | |
| 11 | 2020 | 24 | |
| 12 | 2021 | 22 | |
| 13 | 2022 | 21 | |
| 14 | 2017 | 19 | |
| 15 | 2024 | 19 | |
| 16 | 2016 | 14 | |
| 17 | 2023 | 13 | |
| 18 | 2018 | 11 | |
| 19 | 2024 | 10 | |
| 20 | 2023 | 8 |
About Guilherme Aresta
Guilherme Aresta is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology, Pulmonary and Respiratory Medicine, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 41 papers that have together received 1.9k indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (19 papers), Radiomics and Machine Learning in Medical Imaging (14 papers), Lung Cancer Diagnosis and Treatment (11 papers), AI in cancer detection (10 papers), COVID-19 diagnosis using AI (8 papers), Retinal Diseases and Treatments (7 papers), Digital Imaging for Blood Diseases (5 papers) and Optical Coherence Tomography Applications (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.2k citations), Health Informatics (65 citations), Artificial Intelligence (1.3k citations), Computer Vision and Pattern Recognition (721 citations) and Biophysics (127 citations). Guilherme Aresta has collaborated with scholars based in Portugal, Austria and China. Frequent co-authors include Aurélio Campilho, Teresa Araújo, Catarina Eloy, Paulo Aguiar, António Polónia, José Rouco, Eduardo Castro, Ana Maria Mendonça, Ângela M. Carneiro and Luís Mendonça. Their work appears in journals such as Medical Image Analysis, Scientific Reports, IEEE Journal of Biomedical and Health Informatics, Translational Vision Science & Technology and Expert Systems with Applications.
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.