Kátia D. Pacheco
- Radiology, Nuclear Medicine and Imaging top 2%
- Ophthalmology top 0.5%
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Health Informatics top 1%
- Co-authors
- Neil M. BresslerNeil JoshiPhilippe BurlinaDavid FreundMichael PekalaJun KongWilliam PaulT. Y. Alvin Liu
- Topics
- Retinal Diseases and Treatments (11 papers)Retinal Imaging and Analysis (10 papers)Retinal and Optic Conditions (5 papers)
- Journals
- American Journal of OphthalmologyInvestigative Ophthalmology & Visual ScienceComputers in Biology and Medicine
- Partner nations
- United StatesBrazilArgentina
In The Last Decade
Kátia D. Pacheco
20 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Radiology, Nuclear Medicine and Imaging 860
- Ophthalmology 703
- Computer Vision and Pattern Recognition 199
- Artificial Intelligence 127
- Health Informatics 96
Countries citing papers authored by Kátia D. Pacheco
This map shows the geographic impact of Kátia D. Pacheco'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 Kátia D. Pacheco with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kátia D. Pacheco more than expected).
Fields of papers citing papers by Kátia D. Pacheco
This network shows the impact of papers produced by Kátia D. Pacheco. 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 Kátia D. Pacheco. The network helps show where Kátia D. Pacheco may publish in the future.
Co-authorship network of co-authors of Kátia D. Pacheco
This figure shows the co-authorship network connecting the top 25 collaborators of Kátia D. Pacheco. A scholar is included among the top collaborators of Kátia D. Pacheco 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 Kátia D. Pacheco. Kátia D. Pacheco is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 1 | |
| 3 | 76 | |
| 4 | 46 | |
| 5 | 4 | |
| 6 | 110 | |
| 7 | 20 | |
| 8 | 1 | |
| 9 | 41 | |
| 10 | 116 | |
| 11 | 50 | |
| 12 | Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networksbreakdown → | 426 |
| 13 | 160 | |
| 14 | 12 | |
| 15 | Evaluation of automated drusen detection system for fundus photographs of patients with age-related macular degeneration | 4 |
| 16 | 2 | |
| 17 | 6 | |
| 18 | 5 | |
| 19 | 9 | |
| 20 | 8 |
About Kátia D. Pacheco
Kátia D. Pacheco is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Rheumatology, having authored 20 papers that have together received 1.1k indexed citations. Recurring topics across this work include Retinal Diseases and Treatments (11 papers), Retinal Imaging and Analysis (10 papers) and Retinal and Optic Conditions (5 papers). The work is most often cited by research in Ophthalmology (703 citations), Health Informatics (96 citations) and Radiology, Nuclear Medicine and Imaging (860 citations). Kátia D. Pacheco has collaborated with scholars based in United States, Brazil and Argentina. Frequent co-authors include Neil M. Bressler, Neil Joshi, Philippe Burlina, David Freund, Michael Pekala, Jun Kong, William Paul, T. Y. Alvin Liu, Ian C. Han and Mongkol Tadarati. Their work appears in journals such as American Journal of Ophthalmology, Investigative Ophthalmology & Visual Science and Computers in Biology and Medicine.
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.