Caleb Richter

703 citations
9 papers · 498 · h-index 6

Impact in

Papers in

Caleb Richter

9 papers receiving 480 citations

Peers

Caleb Richter
Comparison fields: 5 of 83
  • Health Informatics 22
  • Radiology, Nuclear Medicine and Imaging 334
  • Artificial Intelligence 356
  • Neurology 61
  • Computer Vision and Pattern Recognition 98
Replace Seyedehnafiseh Mirniaharikandehei with:
Seyedehnafiseh Mirniaharikandehei United States
Yaniv Bar Israel
Hiba Chougrad Morocco
Hamid Zouaki Morocco
Neeraj Dhungel Australia
Tahir Mahmood South Korea
Andrik Rampun United Kingdom
Bibo Shi United States
Gopichandh Danala United States
Luyang Luo Hong Kong
Caleb Richter relative to Seyedehnafiseh Mirniaharikandehei United States Seyedehnafiseh Mirniaharikandehei's profile →
Citations per field
00.5×
Seyedehnafiseh Mirniaharikandehei · 1×
Citations per year

Countries citing papers authored by Caleb Richter

Since Specialization
Citations

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

Fields of papers citing papers by Caleb Richter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Caleb Richter, 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 Caleb Richter Line = papers co-authored together Caleb Richter links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 2018172
2 2017156
3 201876
4 201948
5 202029
6 201811
7 20193
8 20182
9 20181

About Caleb Richter

Caleb Richter is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Surgery and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 498 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (7 papers), COVID-19 diagnosis using AI (3 papers), Digital Radiography and Breast Imaging (3 papers), Bladder and Urothelial Cancer Treatments (1 paper), Digital Imaging for Blood Diseases (1 paper), Lung Cancer Diagnosis and Treatment (1 paper) and Prostate Cancer Diagnosis and Treatment (1 paper). The work is most often cited by research in Health Informatics (22 citations), Radiology, Nuclear Medicine and Imaging (334 citations), Artificial Intelligence (356 citations), Neurology (61 citations) and Computer Vision and Pattern Recognition (98 citations). Caleb Richter has collaborated with scholars based in United States. Frequent co-authors include Ravi K. Samala, Lubomir M. Hadjiiski, Heang‐Ping Chan, Mark A. Helvie, H. Kenny, Ajjai Alva, Alon Z. Weizer, Elaine M. Caoili, Chintana Paramagul and Eric Q. Wu. Their work appears in journals such as Physics in Medicine and Biology, IEEE Transactions on Medical Imaging and Tomography.

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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