World's Best Scientists 2026 revealed!
Jayashree Kalpathy-Cramer

Jayashree Kalpathy-Cramer

D-Index & Metrics

Computer Science

D-Index
64
Citations
29797
World Ranking
2521
National Ranking
1259

Overview

Jayashree Kalpathy-Cramer is affiliated with Harvard University in the United States. Their research primarily focuses on Medicine, with a significant number of publications (407) in this broad field. Within Medicine, their work concentrates on several key subfields including Radiology, Nuclear Medicine and Imaging (176 publications), Artificial Intelligence (64), Pulmonary and Respiratory Medicine (46), Health Informatics (27), and Epidemiology (24).

Their research encompasses a variety of main topics related to artificial intelligence and medical imaging. These topics include:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Retinopathy of Prematurity Studies
  • Artificial Intelligence in Healthcare and Education
  • COVID-19 diagnosis using AI
  • Glioma Diagnosis and Treatment
  • Neonatal and fetal brain pathology

Jayashree Kalpathy-Cramer's recent publications reflect their sustained focus on AI applications in medical imaging and oncology. Selected papers include:

  • "Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging," 2021, Radiology Artificial Intelligence
  • "Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge," 2020, Radiology Artificial Intelligence
  • "Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update," 2024, Radiology Artificial Intelligence
  • "Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas," 2020, Neuro-Oncology
  • "Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study," 2024, The Lancet Oncology

Frequent collaborators add further context to their research network. Key coauthors with whom they have worked extensively include:

  • Praveer Singh
  • Ken Chang
  • J. Peter Campbell
  • Michael F. Chiang
  • R.V. Paul Chan

The scientist's work has appeared in notable publication venues, reflecting the interdisciplinary scope of their research. Frequent venues include:

  • Radiology Artificial Intelligence
  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Ophthalmology Science
  • Neuro-Oncology

Best Publications

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

    Andriy Fedorov;Reinhard Beichel;Jayashree Kalpathy-Cramer;Julien Finet

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials

    Benjamin M. Ellingson;Martin Bendszus;Martin Bendszus;Jerrold Boxerman;Daniel Barboriak

  • Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

    James M. Brown;J. Peter Campbell;Andrew Beers;Ken Chang

  • Introduction to Machine Learning, Neural Networks, and Deep Learning.

    Rene Y. Choi;Aaron S. Coyner;Jayashree Kalpathy-Cramer;Michael F. Chiang

  • The RSNA Pediatric Bone Age Machine Learning Challenge.

    Safwan S. Halabi;Luciano M. Prevedello;Jayashree Kalpathy-Cramer;Artem B. Mamonov

  • Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

    M. Zhou;J. Scott;B. Chaudhury;L. Hall

  • Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging

    Ken Chang;Harrison X Bai;Hao Zhou;Chang Su

  • A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

    Curtis P. Langlotz;Bibb Allen;Bradley J. Erickson;Jayashree Kalpathy-Cramer

  • Distributed deep learning networks among institutions for medical imaging.

    Ken Chang;Niranjan Balachandar;Carson K. Lam;Darvin Yi

  • Methods for Segmentation and Classification of Digital Microscopy Tissue Images.

    Quoc Dang Vu;Simon Graham;Tahsin Kurc;Minh Nguyen Nhat To

  • Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017.

    Jinzhong Yang;Harini Veeraraghavan;Samuel G. Armato;Keyvan Farahani

  • Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

    Adam E Flanders;Luciano M Prevedello;George Shih;Safwan S Halabi

  • Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

    Ken Chang;Andrew L Beers;Harrison X Bai;James M Brown

  • Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013.

    Jayashree Kalpathy-Cramer;Alba Garcia Seco de Herrera;Dina Demner-Fushman;Sameer K. Antani

  • Overview of the CLEF 2009 medical image retrieval track

    Henning Müller;Jayashree Kalpathy-Cramer;Ivan Eggel;Steven Bedrick

  • PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images.

    Samuel G. Armato;Henkjan Huisman;Karen Drukker;Lubomir Hadjiiski

  • Advanced magnetic resonance imaging of the physical processes in human glioblastoma.

    Jayashree Kalpathy-Cramer;Elizabeth R. Gerstner;Kyrre E. Emblem;Kyrre E. Emblem;Ovidiu C. Andronesi

  • ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.

    Stefan Winzeck;Arsany Hakim;Richard McKinley;José A. A. D. S. R. Pinto

  • Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons

    Nancy A Obuchowski;Anthony P Reeves;Erich P Huang;Xiao-Feng Wang

  • Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas.

    Jerrold L. Boxerman;Chad C. Quarles;Leland S. Hu;Bradley J. Erickson

  • Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.

    Travis K Redd;John Peter Campbell;James M Brown;Sang Jin Kim

  • Overview of the ImageCLEF 2013 medical tasks

    Alba Garcia Seco de Herrera;Jayashree Kalpathy-Cramer;Dina Demner-Fushman;Sameer K. Antani

  • Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge

    Wei Huang;Xin Li;Yiyi Chen;Xia Li

Frequent Co-Authors

Deniz Erdogmus
Deniz Erdogmus Northeastern University
Bruce R. Rosen
Bruce R. Rosen Harvard University
Henning Müller
Henning Müller University of Applied Sciences and Arts Western Switzerland
Stratis Ioannidis
Stratis Ioannidis Northeastern University
Jennifer G. Dy
Jennifer G. Dy Northeastern University
William R. Hersh
William R. Hersh Oregon Health & Science University
Daniel L. Rubin
Daniel L. Rubin Stanford University
Rakesh K. Jain
Rakesh K. Jain Harvard University
Paul E. Kinahan
Paul E. Kinahan University of Washington

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