Jayashree Kalpathy–Cramer

31.4k total citations · 2 hit papers
237 papers, 12.0k citations indexed

About

Jayashree Kalpathy–Cramer is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Jayashree Kalpathy–Cramer has authored 237 papers receiving a total of 12.0k indexed citations (citations by other indexed papers that have themselves been cited), including 157 papers in Radiology, Nuclear Medicine and Imaging, 41 papers in Pulmonary and Respiratory Medicine and 39 papers in Artificial Intelligence. Recurrent topics in Jayashree Kalpathy–Cramer's work include Radiomics and Machine Learning in Medical Imaging (54 papers), Retinopathy of Prematurity Studies (48 papers) and Neonatal and fetal brain pathology (38 papers). Jayashree Kalpathy–Cramer is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (54 papers), Retinopathy of Prematurity Studies (48 papers) and Neonatal and fetal brain pathology (38 papers). Jayashree Kalpathy–Cramer collaborates with scholars based in United States, Switzerland and Spain. Jayashree Kalpathy–Cramer's co-authors include John M. Buatti, Andriy Fedorov, Dominique Jennings, Fiona Fennessy, Stephen Aylward, Jean‐Christophe Fillion‐Robin, Ron Kikinis, Milan Sonka, Christian Bauer and James V. Miller and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Jayashree Kalpathy–Cramer

227 papers receiving 11.8k citations

Hit Papers

align trajectories sort by overperformance

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.

3D Slicer as an image computing platform for the Quantitative Imaging Network

2012 5.8k citations Andriy Fedorov, Jayashree Kalpathy–Cramer et al. profile →
Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

2018 396 citations J. Peter Campbell, Andrew Beers et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Jayashree Kalpathy–Cramer	6.0k	2.2k	2.2k	1.6k	1.5k	237	12.0k
Namkug Kim	4.6k 0.8×	2.9k 1.3×	1.9k 0.9×	1.3k 0.8×	1.2k 0.8×	412	10.9k
Bradley J. Erickson	4.5k 0.8×	1.5k 0.7×	1.4k 0.6×	895 0.6×	1.8k 1.2×	280	10.4k
Andriy Fedorov	7.5k 1.2×	3.3k 1.5×	3.0k 1.4×	1.7k 1.1×	1.0k 0.7×	94	12.0k
Steve Pieper	7.1k 1.2×	2.7k 1.2×	3.1k 1.4×	2.0k 1.3×	988 0.7×	74	12.5k
Issam El Naqa	8.3k 1.4×	6.0k 2.7×	2.4k 1.1×	1.6k 1.0×	1.8k 1.2×	349	14.9k
Sandy Napel	5.9k 1.0×	3.6k 1.6×	1.9k 0.9×	1.0k 0.7×	1.3k 0.9×	190	9.8k
Jean‐Christophe Fillion‐Robin	6.2k 1.0×	2.5k 1.1×	2.6k 1.2×	1.6k 1.0×	815 0.5×	14	10.1k
Ahmed Hosny	7.4k 1.2×	2.8k 1.2×	2.3k 1.0×	780 0.5×	2.2k 1.5×	50	10.1k
Kaori Togashi	7.8k 1.3×	2.8k 1.3×	1.8k 0.8×	3.2k 2.0×	1.1k 0.7×	561	20.1k
Sébastien Ourselin	10.2k 1.7×	1.7k 0.8×	4.7k 2.2×	1.6k 1.0×	2.8k 1.9×	812	26.7k

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Gomez, Felicia, Arpad Danos, Guilherme Del Fiol, et al.. (2024). A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges. Cancer Discovery. 14(10). 1774–1778.

Kitamura, Felipe, Luciano M. Prevedello, Errol Colak, et al.. (2024). Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges. Radiology Artificial Intelligence. 6(3). e230227–e230227. 6 indexed citations

Sanjosé, Sílvia de, Rebecca B. Perkins, Nicole G. Campos, et al.. (2024). Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy. eLife. 12. 1 indexed citations

Hoebel, Katharina, Christopher P. Bridge, Sara Ahmed, et al.. (2023). Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiology Artificial Intelligence. 6(1). e220231–e220231. 4 indexed citations

Sanjosé, Sílvia de, Rebecca B. Perkins, Nicole G. Campos, et al.. (2023). Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy. eLife. 12. 12 indexed citations

Egemen, Didem, Rebecca B. Perkins, Li C. Cheung, et al.. (2023). Artificial intelligence–based image analysis in clinical testing: lessons from cervical cancer screening. JNCI Journal of the National Cancer Institute. 116(1). 26–33. 25 indexed citations

Cole, Emily, Nita Valikodath, Tala Al-Khaled, et al.. (2022). Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia. SHILAP Revista de lepidopterología. 2(4). 100165–100165. 9 indexed citations

Li, Matthew, Nishanth Arun, Mehak Aggarwal, et al.. (2022). Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19. Medicine. 101(29). e29587–e29587. 10 indexed citations

Bridge, Christopher P., Till D. Best, Maria Wróbel, et al.. (2022). A Fully Automated Deep Learning Pipeline for Multi–Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans. Radiology Artificial Intelligence. 4(1). e210080–e210080. 27 indexed citations

10.

Tabari, Azadeh, et al.. (2022). Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease. PLoS ONE. 17(11). e0277507–e0277507. 9 indexed citations

11.

Chavva, Isha R., Anna Crawford, Mercy H. Mazurek, et al.. (2022). Deep Learning Applications for Acute Stroke Management. Annals of Neurology. 92(4). 574–587. 33 indexed citations

12.

Bridge, Christopher P., Steven C. Pieper, Jochen K. Lennerz, et al.. (2022). Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology. Journal of Digital Imaging. 35(6). 1719–1737. 9 indexed citations

13.

Patel, Jay, Bernardo C. Bizzo, Daniel I. Glazer, et al.. (2022). Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology. 306(2). e220101–e220101. 23 indexed citations

14.

Reid, Nicholas, John Panagides, John Di Capua, et al.. (2022). Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease. CardioVascular and Interventional Radiology. 45(5). 633–640. 14 indexed citations

15.

Arun, Nishanth, Nathan Gaw, Praveer Singh, et al.. (2021). Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. Radiology Artificial Intelligence. 3(6). e200267–e200267. 147 indexed citations

16.

Gerstner, Elizabeth R., Kyrre E. Emblem, Ken Chang, et al.. (2019). Bevacizumab Reduces Permeability and Concurrent Temozolomide Delivery in a Subset of Patients with Recurrent Glioblastoma. Clinical Cancer Research. 26(1). 206–212. 40 indexed citations

17.

Prevedello, Luciano M., Safwan S. Halabi, George Shih, et al.. (2019). Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions. Radiology Artificial Intelligence. 1(1). e180031–e180031. 107 indexed citations

18.

Pan, Ian, Hans Henrik Thodberg, Safwan S. Halabi, Jayashree Kalpathy–Cramer, & David B. Larson. (2019). Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge. Radiology Artificial Intelligence. 1(6). e190053–e190053. 39 indexed citations

19.

Halabi, Safwan S., Luciano M. Prevedello, Jayashree Kalpathy–Cramer, et al.. (2018). The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology. 290(2). 498–503. 283 indexed citations

20.

Zhou, Mu, Jacob G. Scott, Baishali Chaudhury, et al.. (2017). Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. American Journal of Neuroradiology. 39(2). 208–216. 272 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.

Explore authors with similar magnitude of impact