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
1260

Jayashree Kalpathy-Cramer publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jayashree Kalpathy-Cramer sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 347 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jayashree Kalpathy-Cramer D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jayashree Kalpathy-Cramer sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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
Tracy T. Batchelor
Tracy T. Batchelor Brigham and Women's Hospital
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
Patrick Y. Wen
Patrick Y. Wen Harvard University
Daniel L. Rubin
Daniel L. Rubin Stanford University
David I. Rosenthal
David I. Rosenthal The University of Texas MD Anderson Cancer Center

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