Caleb Richter

703 total citations
9 papers, 498 citations indexed

About

Caleb Richter is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Caleb Richter has authored 9 papers receiving a total of 498 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Caleb Richter's work include Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (7 papers) and COVID-19 diagnosis using AI (3 papers). Caleb Richter is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (7 papers) and COVID-19 diagnosis using AI (3 papers). Caleb Richter collaborates with scholars based in United States. Caleb Richter's co-authors include Heang‐Ping Chan, Lubomir M. Hadjiiski, Ravi K. Samala, Mark A. Helvie, H. Kenny, Chintana Paramagul, Ajjai Alva, Eric Q. Wu, Alon Z. Weizer and Elaine M. Caoili and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Physics in Medicine and Biology and Tomography.

In The Last Decade

Caleb Richter

9 papers receiving 480 citations

Peers

Caleb Richter
Cai Chang China
Seyedehnafiseh Mirniaharikandehei United States
Gopichandh Danala United States
Jie-Zhi Cheng Taiwan
Aly A. Mohamed United States
Yaniv Bar Israel
Sarfaraz Hussein United States
Tahir Mahmood South Korea
Morteza Heidari United States
Anup Sadhu India
Cai Chang China
Caleb Richter
Citations per year, relative to Caleb Richter Caleb Richter (= 1×) peers Cai Chang

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-authorship network of co-authors of Caleb Richter

This figure shows the co-authorship network connecting the top 25 collaborators of Caleb Richter. A scholar is included among the top collaborators of Caleb Richter 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 Caleb Richter. Caleb Richter is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Samala, Ravi K., Heang‐Ping Chan, Lubomir M. Hadjiiski, Mark A. Helvie, & Caleb Richter. (2020). Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis. Physics in Medicine and Biology. 65(10). 105002–105002. 29 indexed citations
2.
Samala, Ravi K., Heang‐Ping Chan, Lubomir M. Hadjiiski, et al.. (2019). Analysis of deep convolutional features for detection of lung nodules in computed tomography. 25–25. 3 indexed citations
3.
Wu, Eric Q., Lubomir M. Hadjiiski, Ravi K. Samala, et al.. (2019). Deep Learning Approach for Assessment of Bladder Cancer Treatment Response. Tomography. 5(1). 201–208. 48 indexed citations
4.
Samala, Ravi K., Heang‐Ping Chan, Lubomir M. Hadjiiski, et al.. (2018). Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Physics in Medicine and Biology. 63(9). 95005–95005. 76 indexed citations
5.
Samala, Ravi K., Heang‐Ping Chan, Lubomir M. Hadjiiski, et al.. (2018). Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. 25–25. 11 indexed citations
6.
Samala, Ravi K., Heang‐Ping Chan, Lubomir M. Hadjiiski, et al.. (2018). Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets. IEEE Transactions on Medical Imaging. 38(3). 686–696. 172 indexed citations
7.
Samala, Ravi K., Heang‐Ping Chan, Lubomir M. Hadjiiski, et al.. (2018). Compression of deep convolutional neural network for computer-aided diagnosis of masses in digital breast tomosynthesis. 72–72. 1 indexed citations
8.
Richter, Caleb, Ravi K. Samala, Heang‐Ping Chan, Lubomir M. Hadjiiski, & H. Kenny. (2018). Generalization error analysis: deep convolutional neural network in mammography. 62. 71–71. 2 indexed citations
9.
Samala, Ravi K., Heang‐Ping Chan, Lubomir M. Hadjiiski, et al.. (2017). Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Physics in Medicine and Biology. 62(23). 8894–8908. 156 indexed citations

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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2026