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Harini Veeraraghavan 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 Harini Veeraraghavan 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+

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

Harini Veeraraghavan 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 Harini Veeraraghavan 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+

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

Overview

Harini Veeraraghavan is affiliated with Memorial Sloan Kettering Cancer Center in the United States, focusing on research within the field of medicine, particularly in radiology, nuclear medicine, and imaging. Their work intersects several subfields including radiation, pulmonary and respiratory medicine, oncology, and computer vision and pattern recognition.

Their research broadly covers topics related to radiomics and machine learning in medical imaging, advanced radiotherapy techniques, and medical imaging techniques and applications. Specific emphasis is placed on lung cancer diagnosis and treatment, advanced X-ray and CT imaging, medical image segmentation techniques, and the application of artificial intelligence in cancer detection.

Frequent co-authors collaborating with Veeraraghavan include Joseph O. Deasy, Jue Jiang, Maria Thor, Andreas Rimner, and Sharif Elguindi, reflecting ongoing research partnerships.

Veeraraghavan has published extensively, with a choice selection of recent papers detailed below:

  • Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer, 2022, Nature Cancer
  • A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy, 2020, Breast Cancer Research
  • Artificial Intelligence in CT and MR Imaging for Oncological Applications, 2023, Cancers
  • Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers, 2020, Scientific Reports
  • Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy, 2020, Abdominal Radiology

Frequent publication venues for Veeraraghavan include arXiv (Cornell University), International Journal of Radiation Oncology*Biology*Physics, Medical Physics, Physics and Imaging in Radiation Oncology, and Physics in Medicine and Biology.

Best Publications

  • Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

    Duc Fehr;Harini Veeraraghavan;Andreas Wibmer;Tatsuo Gondo

  • Vision 20/20: Perspectives on automated image segmentation for radiotherapy

    Gregory Sharp;Karl D. Fritscher;Vladimir Pekar;Marta Peroni

  • Computer vision algorithms for intersection monitoring

    H. Veeraraghavan;O. Masoud;N.P. Papanikolopoulos

  • GBM Volumetry using the 3D Slicer Medical Image Computing Platform

    Jan Egger;Tina Kapur;Andriy Fedorov;Steve Pieper

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

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

  • Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images

    Jue Jiang;Yu-Chi Hu;Chia-Ju Liu;Darragh Halpenny

  • Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation.

    Jue Jiang;Yu-Chi Hu;Neelam Tyagi;Pengpeng Zhang

  • Technical note: Extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research

    Aditya P. Apte;Aditi Iyer;Mireia Crispin-Ortuzar;Mireia Crispin-Ortuzar;Rutu Pandya

  • Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy.

    Sharif Elguindi;Michael J. Zelefsky;Jue Jiang;Harini Veeraraghavan

  • Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets.

    Hyemin Um;Florent Tixier;Dalton Bermudez;Joseph O Deasy

  • Patch-based generative adversarial neural network models for head and neck MR-only planning.

    Peter Klages;Ilyes Benslimane;Sadegh Riyahi;Jue Jiang

  • A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy

    Elizabeth J. Sutton;Natsuko Onishi;Duc A. Fehr;Brittany Z. Dashevsky

  • Communication Strategies in Multi-robot Search and Retrieval: Experiences with MinDART

    Paul E. Rybski;Amy C. Larson;Harini Veeraraghavan;Monica A. LaPoint

  • Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers.

    Harini Veeraraghavan;Claire F. Friedman;Claire F. Friedman;Deborah F. DeLair;Deborah F. DeLair;Josip Ninčević

  • Robust target detection and tracking through integration of motion, color, and geometry

    Harini Veeraraghavan;Paul Schrater;Nikos Papanikolopoulos

  • Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features

    Florent Tixier;Hyemin Um;Robert J Young;Harini Veeraraghavan

  • Classifiers for driver activity monitoring

    Harini Veeraraghavan;Nathaniel Bird;Stefan Atev;Nikolaos Papanikolopoulos

  • Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations.

    Carlos E. Cardenas;Abdallah S. R. Mohamed;Jinzhong Yang;Mark Gooding

  • Cross‐modality (CT‐MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets

    Jue Jiang;Yu-Chi Hu;Neelam Tyagi;Pengpeng Zhang

  • PSIGAN: Joint Probabilistic Segmentation and Image Distribution Matching for Unpaired Cross-Modality Adaptation-Based MRI Segmentation

    Jue Jiang;Yu-Chi Hu;Neelam Tyagi;Andreas Rimner

Frequent Co-Authors

Nikolaos Papanikolopoulos
Nikolaos Papanikolopoulos University of Minnesota
Paul Schrater
Paul Schrater University of Minnesota
Hedvig Hricak
Hedvig Hricak Memorial Sloan Kettering Cancer Center
Gregory C. Sharp
Gregory C. Sharp Harvard University
Manuela Veloso
Manuela Veloso Carnegie Mellon University
Maria Gini
Maria Gini University of Minnesota
Taha Merghoub
Taha Merghoub Cornell University
Jedd D. Wolchok
Jedd D. Wolchok Cornell University
Alexandra J. Golby
Alexandra J. Golby Brigham and Women's Hospital
Michael Gill
Michael Gill Trinity College Dublin

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