Jared Dunnmon

2.1k citations
23 papers · 908 indexed · h-index 14

Impact in

Papers in

Jared Dunnmon

23 papers receiving 890 citations

Peers

Jared Dunnmon
Comparison fields: 5 of 110
  • Health Informatics 102
  • Internal Medicine 64
  • Radiology, Nuclear Medicine and Imaging 284
  • Artificial Intelligence 282
  • Computational Mechanics 143
Replace Pulkit Agrawal with:
Pulkit Agrawal United States
Lucian Itu Romania
David Harmon United States
Jai Prashanth Rao Singapore
Danni Ai China
Jingfan Fan China
Jianhuang Wu China
A.P. Loh Singapore
Qianwei Zhou China
Hyunkwang Lee United States
Jared Dunnmon relative to Pulkit Agrawal United States Pulkit Agrawal's profile →
Citations per field
00.5×10×15×21.3×
Pulkit Agrawal · 1×
Citations per year

Countries citing papers authored by Jared Dunnmon

Since Specialization
Citations

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

Fields of papers citing papers by Jared Dunnmon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jared Dunnmon, 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 Jared Dunnmon Line = papers co-authored together Jared Dunnmon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011183
2 2018150
3 2020103
4 201976
5 201956
6 201949
7 202047
8 201647
9
Learning to Compose Domain-Specific Transformations for Data Augmentation.
201735
10 202125
11 201822
12 202221
13 202020
14 202317
15 201713
16 202011
17 20199
18 20217
19 20217
20 20155

About Jared Dunnmon

Jared Dunnmon is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Health Informatics, Pulmonary and Respiratory Medicine and Pediatrics, Perinatology and Child Health, having authored 23 papers that have together received 908 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), Artificial Intelligence in Healthcare and Education (4 papers), COVID-19 diagnosis using AI (3 papers), Neonatal and fetal brain pathology (2 papers), AI in cancer detection (2 papers), Radiative Heat Transfer Studies (2 papers), Combustion and flame dynamics (2 papers) and Radiology practices and education (2 papers). The work is most often cited by research in Health Informatics (102 citations), Internal Medicine (64 citations), Radiology, Nuclear Medicine and Imaging (284 citations), Artificial Intelligence (282 citations) and Computational Mechanics (143 citations). Jared Dunnmon has collaborated with scholars based in United States, Sweden and Saudi Arabia. Frequent co-authors include Christopher Ré, Samuel C. Stanton, Brian P. Mann, Earl H. Dowell, Matthew P. Lungren, Daniel L. Rubin, Curtis P. Langlotz, Darvin Yi, Alexander Ratner and Bhavik N. Patel. Their work appears in journals such as Nature Communications, npj Digital Medicine, Radiology Artificial Intelligence, Radiologic Clinics of North America and IEEE Journal of Biomedical and Health Informatics.

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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