Brian Befano

44 papers receiving 1.8k citations

Brian Befano's Hit Papers

An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening 2018 · 283 citations
2830+2+5Years since publication50100150200250

Peers

Brian Befano
Comparison fields: 5 of 100
  • Epidemiology 1.4k
  • Health Informatics 45
  • Microbiology 104
  • Obstetrics and Gynecology 115
  • Oncology 251
Replace Maria Demarco with:
Maria Demarco United States
Nancy Poitras United States
Sanjay Gupta India
Ioannis Panayiotides Greece
Michael J. Thrall United States
Didem Egemen United States
Robert A. Goulart United States
Pamela Michelow South Africa
Dirk van Niekerk Canada
Adela Saco Spain
Brian Befano relative to Maria Demarco United States Maria Demarco's profile →
Citations per field
00.5×10×20×28.5×
Maria Demarco · 1×
Citations per year

Countries citing papers authored by Brian Befano

Since Specialization
Citations

This map shows the geographic impact of Brian Befano'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 Brian Befano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Befano more than expected).

Fields of papers citing papers by Brian Befano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Befano. 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 Brian Befano. The network helps show where Brian Befano may publish in the future.

Co-authors

The 25 scholars most cited alongside Brian Befano, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Brian Befano Line = papers co-authored together Brian Befano links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1
An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening
Hit paper breakdown →
2018283
2 2010206
3 2020140
4 2017117
5 201483
6 201077
7 201876
8 201664
9 202051
10 202050
11 201749
12 202148
13 202246
14 201546
15 201744
16 201540
17 202137
18 201533
19 202331
20 202222

About Brian Befano

Brian Befano is a scholar working on Epidemiology, Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging and Surgery, having authored 48 papers that have together received 1.8k indexed citations. Recurring topics across this work include Cervical Cancer and HPV Research (43 papers), AI in cancer detection (13 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Global Cancer Incidence and Screening (5 papers), Genital Health and Disease (5 papers), Reproductive tract infections research (4 papers), Molecular Biology Techniques and Applications (4 papers) and Artificial Intelligence in Healthcare (1 paper). The work is most often cited by research in Epidemiology (1.4k citations), Health Informatics (45 citations), Microbiology (104 citations), Obstetrics and Gynecology (115 citations) and Oncology (251 citations). Brian Befano has collaborated with scholars based in United States, Spain and Costa Rica. Frequent co-authors include Mark Schiffman, Nicolas Wentzensen, Julia C. Gage, Philip E. Castle, Ana Cecilia Rodríguez, Li C. Cheung, Thomas Lorey, Nancy Poitras, Hormuzd A. Katki and Maria Demarco. Their work appears in journals such as JNCI Journal of the National Cancer Institute, International Journal of Cancer, Gynecologic Oncology, Journal of Lower Genital Tract Disease and Journal of Clinical Microbiology.

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.

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