Jayashree Kalpathy-Cramer

993 total citations · 1 hit paper
10 papers, 41 citations indexed

About

Jayashree Kalpathy-Cramer is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Epidemiology. According to data from OpenAlex, Jayashree Kalpathy-Cramer has authored 10 papers receiving a total of 41 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 2 papers in Health Informatics and 2 papers in Epidemiology. Recurrent topics in Jayashree Kalpathy-Cramer's work include Radiomics and Machine Learning in Medical Imaging (5 papers), COVID-19 diagnosis using AI (4 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Jayashree Kalpathy-Cramer is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), COVID-19 diagnosis using AI (4 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Jayashree Kalpathy-Cramer collaborates with scholars based in United States, Canada and United Kingdom. Jayashree Kalpathy-Cramer's co-authors include Christopher P. Bridge, Elizabeth R. Gerstner, Eric Yang, Matthew Li, Min Lang, Francis Deng, Ambrose J. Huang, Marc D. Succi, Berkman Sahiner and Weijie Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Ophthalmology and British Journal of Radiology.

In The Last Decade

Jayashree Kalpathy-Cramer

6 papers receiving 40 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A review of deep learning for brain tumor analysis in MRI

2025 16 citations Jayashree Kalpathy-Cramer, Elizabeth R. Gerstner et al. npj Precision Oncology profile →

Peers

Jayashree Kalpathy-Cramer

AI

RNMI

NEURO

CVPR

HI

Dongang Wang Australia

Surbhi Bhatnagar United States

Moritz Knolle Germany

Sen Zha China

Maximilian Zenk Germany

Avinash Kori United Kingdom

L. Escudero United Kingdom

Rashindrie Perera Australia

Kaiqi Dong China

Dongang Wang Australia

Jayashree Kalpathy-Cramer

21 ×1.4

AI

10 ×0.7

RNMI

9 ×0.6

NEURO

13 ×1.3

CVPR

4 ×0.8

HI

Citations per year, relative to Jayashree Kalpathy-Cramer Jayashree Kalpathy-Cramer (= 1×) peers Dongang Wang

Countries citing papers authored by Jayashree Kalpathy-Cramer

Since Specialization

Citations

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

Fields of papers citing papers by Jayashree Kalpathy-Cramer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jayashree Kalpathy-Cramer. 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 Jayashree Kalpathy-Cramer. The network helps show where Jayashree Kalpathy-Cramer may publish in the future.

Co-authorship network of co-authors of Jayashree Kalpathy-Cramer

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

All Works

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10 of 10 papers shown

1.

Kalpathy-Cramer, Jayashree, et al.. (2025). A review of deep learning for brain tumor analysis in MRI. npj Precision Oncology. 9(1). 2–2. 16 indexed citations breakdown →

2.

Clark, Christopher, et al.. (2025). Automating the Detection of Acetowhite Lesions by Classifying the Temporal Behavior of Cervical Regions. Journal of Lower Genital Tract Disease. 30(1). 31–38.

3.

Bridge, Christopher P., et al.. (2025). Re-identification of patients from imaging features extracted by foundation models. npj Digital Medicine. 8(1). 469–469.

4.

Armato, Samuel G., Karen Drukker, Lubomir M. Hadjiiski, et al.. (2025). MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs. Journal of Medical Imaging. 12(2). 24505–24505.

5.

Clark, Christopher, Talisa E. de Carlo, Naresh Mandava, et al.. (2025). Optic Cup and Disc Segmentation of Fundus Images Using Artificial Intelligence Externally Validated With Optical Coherence Tomography Measurements. Translational Vision Science & Technology. 14(6). 30–30.

6.

Coyner, Aaron S., Benjamin K. Young, Susan Ostmo, et al.. (2024). Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity. Ophthalmology. 131(11). 1290–1296. 4 indexed citations

7.

Egemen, Didem, Brian Befano, Ana Cecilia Rodríguez, et al.. (2024). Assessing generalizability of an AI-based visual test for cervical cancer screening. SHILAP Revista de lepidopterología. 3(10). e0000364–e0000364. 2 indexed citations

8.

Whitney, Heather M., Kyle J. Myers, Karen Drukker, et al.. (2023). Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons. Journal of Medical Imaging. 10(6). 61105–61105. 4 indexed citations

9.

Kalpathy-Cramer, Jayashree, et al.. (2023). Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs. 8. 13–13. 1 indexed citations

10.

Yang, Eric, Matthew Li, Francis Deng, et al.. (2023). Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?. British Journal of Radiology. 96(1149). 20220769–20220769. 14 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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