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, Dominique Jennings, Andriy Fedorov, Sonia Pujol, Stephen Aylward, Ron Kikinis, Fiona Fennessy, Steve Pieper, Jean‐Christophe Fillion‐Robin and Milan Sonka 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

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 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 →

Peers

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

#	Work	Indexed citations
1	A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges 2024 ·Cancer Discovery ·Felicia Gomez, Arpad Danos, Guilherme Del Fiol, Anant Madabhushi, Pallavi Tiwari, Joshua F. McMichael, Spyridon Bakas, Jiang Bian, Christos Davatzikos, Elana J. Fertig, Jayashree Kalpathy–Cramer, (unknown), Guergana Savova, Meliha Yetişgen, Eliezer M. Van Allen, Jeremy L. Warner, Fred Prior, Malachi Griffith, Obi L. Griffith	0
2	Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy 2024 ·eLife ·Sílvia de Sanjosé, Rebecca B. Perkins, Nicole G. Campos, Federica Inturrisi, Didem Egemen, Brian Befano, Ana Cecilia Rodríguez, José Jerónimo, Li C. Cheung, Kanan Desai, Paul K. J. Han, Akiva P. Novetsky, (unknown), Jenna Marcus, (unknown), Nicolas Wentzensen, Jayashree Kalpathy–Cramer, Mark Schiffman, (unknown)	1
3	Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation 2023 ·Radiology Artificial Intelligence ·Katharina Hoebel, Christopher P. Bridge, Sara Ahmed, (unknown), Caroline Chung, Raymond Y. Huang, Jason M. Johnson, Albert Kim, (unknown), Ken Chang, Jay Patel, Marco C. Pinho, Tracy T. Batchelor, Bruce R. Rosen, Elizabeth R. Gerstner, Jayashree Kalpathy–Cramer	4
4	Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy 2023 ·eLife ·Sílvia de Sanjosé, Rebecca B. Perkins, Nicole G. Campos, Federica Inturrisi, Didem Egemen, Brian Befano, Ana Cecilia Rodríguez, José Jerónimo, Li C. Cheung, Kanan Desai, Paul K. J. Han, Akiva P. Novetsky, (unknown), Jenna Marcus, (unknown), Nicolas Wentzensen, Jayashree Kalpathy–Cramer, Mark Schiffman, (unknown)	12
5	Artificial intelligence–based image analysis in clinical testing: lessons from cervical cancer screening 2023 ·JNCI Journal of the National Cancer Institute ·Didem Egemen, Rebecca B. Perkins, Li C. Cheung, Brian Befano, Ana Cecilia Rodríguez, Kanan Desai, (unknown), (unknown), Sameer Antani, José Jerónimo, Nicolas Wentzensen, Jayashree Kalpathy–Cramer, Sílvia de Sanjosé, Mark Schiffman	25
6	Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19 2022 ·Medicine ·Matthew Li, Nishanth Arun, Mehak Aggarwal, Sharut Gupta, Praveer Singh, Brent P. Little, Dexter P. Mendoza, (unknown), (unknown), Suely Fazio Ferraciolli, Marc D. Succi, Min Lang, Bernardo C. Bizzo, Ittai Dayan, Felipe Kitamura, Jayashree Kalpathy–Cramer	10
7	Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia 2022 ·SHILAP Revista de lepidopterología ·Emily Cole, Nita Valikodath, Tala Al-Khaled, Sanyam Bajimaya, (unknown), Tsengelmaa Chuluunbat, (unknown), Karyn Jonas, Chimgee Chuluunkhuu, Leslie MacKeen, Vivien Yap, Joelle Hallak, Susan Ostmo, Wei‐Chi Wu, Aaron S. Coyner, Praveer Singh, Jayashree Kalpathy–Cramer, Michael F. Chiang, J. Peter Campbell, R.V. Paul Chan	9
8	Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease 2022 ·PLoS ONE ·(unknown), (unknown), Azadeh Tabari, Sanjeeva P. Kalva, Jayashree Kalpathy–Cramer, Dania Daye	9
9	A Fully Automated Deep Learning Pipeline for Multi–Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans 2022 ·Radiology Artificial Intelligence ·Christopher P. Bridge, Till D. Best, Maria Wróbel, (unknown), Kirti Magudia, (unknown), Jonathan H. Chung, Jayashree Kalpathy–Cramer, Katherine P. Andriole, Florian J. Fintelmann	27
10	Deep Learning Applications for Acute Stroke Management 2022 ·Annals of Neurology ·Isha R. Chavva, Anna Crawford, Mercy H. Mazurek, Matthew Ming Fai Yuen, Anjali M. Prabhat, Seyedmehdi Payabvash, Gordon Sze, Guido J. Falcone, Charles Matouk, Adam de Havenon, Jennifer A. Kim, Richa Sharma, Steven J. Schiff, Matthew S. Rosen, Jayashree Kalpathy–Cramer, (unknown), W. Taylor Kimberly, Kevin N. Sheth	33
11	Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT 2022 ·Radiology ·(unknown), Jay Patel, Bernardo C. Bizzo, Daniel I. Glazer, Christopher P. Bridge, Katherine P. Andriole, Borna E. Dabiri, (unknown), Keith J. Dreyer, Jayashree Kalpathy–Cramer, William W. Mayo-Smith	23
12	ACR’s Connect and AI-LAB technical framework 2022 ·JAMIA Open ·(unknown), Laura P. Coombs, (unknown), (unknown), (unknown), Kendall Schmidt, (unknown), (unknown), (unknown), Ken Chang, Jayashree Kalpathy–Cramer	5
13	Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease 2022 ·CardioVascular and Interventional Radiology ·(unknown), Nicholas Reid, John Panagides, John Di Capua, Charles DeCarlo, Anahita Dua, Sanjeeva P. Kalva, Jayashree Kalpathy–Cramer, Dania Daye	14
14	Severity of Chest Imaging is Correlated with Risk of Acute Neuroimaging Findings among Patients with COVID-19 2021 ·American Journal of Neuroradiology ·Min Lang, Matthew Li, (unknown), Byung Chul Yoon, Dexter P. Mendoza, Efrén J. Flores, Sandra Rincon, William A. Mehan, John Conklin, Susie Y. Huang, (unknown), (unknown), Thabele M Leslie‐Mazwi, Jayashree Kalpathy–Cramer, Brent P. Little, Karen Buch	8
15	Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging 2021 ·Radiology Artificial Intelligence ·Nishanth Arun, Nathan Gaw, Praveer Singh, Ken Chang, Mehak Aggarwal, (unknown), Katharina Hoebel, Sharut Gupta, Jay Patel, Mishka Gidwani, Julius Adebayo, Matthew Li, Jayashree Kalpathy–Cramer	147
16	Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks 2020 ·Radiology Artificial Intelligence ·Matthew Li, Nishanth Arun, Mishka Gidwani, Ken Chang, Francis Deng, Brent P. Little, Dexter P. Mendoza, Min Lang, Susanna I. Lee, Aileen O’Shea, Anushri Parakh, Praveer Singh, Jayashree Kalpathy–Cramer	105
17	Bevacizumab Reduces Permeability and Concurrent Temozolomide Delivery in a Subset of Patients with Recurrent Glioblastoma 2019 ·Clinical Cancer Research ·Elizabeth R. Gerstner, Kyrre E. Emblem, Ken Chang, (unknown), Yi‐Fen Yen, Andrew Beers, Jörg Dietrich, Scott R. Plotkin, Ciprian Catana, Jacob M. Hooker, Dan G. Duda, Bruce R. Rosen, Jayashree Kalpathy–Cramer, Rakesh K. Jain, Tracy T. Batchelor	40
18	Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge 2019 ·Radiology Artificial Intelligence ·Ian Pan, Hans Henrik Thodberg, Safwan S. Halabi, Jayashree Kalpathy–Cramer, David B. Larson	39
19	The RSNA Pediatric Bone Age Machine Learning Challenge 2018 ·Radiology ·Safwan S. Halabi, Luciano M. Prevedello, Jayashree Kalpathy–Cramer, Artem B. Mamonov, Alexander Bilbily, Mark Cicero, Ian Pan, Lucas Pereira, Rafael T. Sousa, Nitamar Abdala, Felipe Kitamura, Hans Henrik Thodberg, Leon Chen, George Shih, Katherine P. Andriole, Marc Kohli, Bradley J. Erickson, Adam E. Flanders	283
20	Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches 2017 ·American Journal of Neuroradiology ·Mu Zhou, Jacob G. Scott, Baishali Chaudhury, Lawrence Hall, Dmitry B. Goldgof, Kristen W. Yeom, Michael Iv, Yangming Ou, Jayashree Kalpathy–Cramer, Sandy Napel, Robert J. Gillies, Olivier Gevaert, Robert A. Gatenby	272

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