World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
22121
World Ranking
3341
National Ranking
200

Nasir M. Rajpoot 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 Nasir M. Rajpoot 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: 279 publications — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Nasir M. Rajpoot 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 Nasir M. Rajpoot 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

Nasir M. Rajpoot is affiliated with the University of Warwick in the United Kingdom. Their research spans the intersection of computer science and medicine, with a strong focus on artificial intelligence applications in medical imaging and cancer detection. The main fields of study include Computer Science and Medicine, with notable involvement in subfields such as Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Oncology, and Biophysics.

Rajpoot has contributed to several topics of research including:

  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Digital Imaging for Blood Diseases
  • Cell Image Analysis Techniques
  • Colorectal Cancer Screening and Detection
  • Oral Health Pathology and Treatment
  • Cancer Immunotherapy and Biomarkers

Their recent significant papers include:

  • Metrics reloaded: recommendations for image analysis validation (2024, Nature Methods)
  • Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study (2021, The Lancet Digital Health)
  • Cellular community detection for tissue phenotyping in colorectal cancer histology images (2020, Medical Image Analysis)
  • PanNuke Dataset Extension, Insights and Baselines (2020, arXiv (Cornell University))
  • Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review (2020, Oral Oncology)

Rajpoot collaborates frequently with a set of coauthors, most notably:

  • Fayyaz Minhas
  • Shan E Ahmed Raza
  • Mostafa Jahanifar
  • Simon Graham
  • David Snead

Their work is published extensively in several venues, highlighting a large body of contributions in areas related to medical image analysis and computational pathology. Frequent publication venues include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Medical Image Analysis
  • The Journal of Pathology
  • British Journal of Cancer

Best Publications

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • Histopathological Image Analysis: A Review

    M.N. Gurcan;L.E. Boucheron;A. Can;A. Madabhushi

  • Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

    Korsuk Sirinukunwattana;Shan E Ahmed Raza;Yee-Wah Tsang;David R. J. Snead

  • Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

    Simon Graham;Quoc Dang Vu;Shan E. Ahmed Raza;Ayesha Azam

  • Gland segmentation in colon histology images: The GlaS challenge contest

    Korsuk Sirinukunwattana;Josien P.W. Pluim;Hao Chen;Xiaojuan Qi

  • Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

    Haroon Idrees;Muhmmad Tayyab;Kishan Athrey;Dong Zhang

  • A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

    Adnan Mujahid Khan;Nasir Rajpoot;Darren Treanor;Derek Magee

  • Assessment of algorithms for mitosis detection in breast cancer histopathology images

    Mitko Veta;Paul J. van Diest;Stefan M. Willems;Haibo Wang

  • A Multi-Organ Nucleus Segmentation Challenge

    Neeraj Kumar;Ruchika Verma;Deepak Anand;Yanning Zhou

  • Why rankings of biomedical image analysis competitions should be interpreted with care

    Lena Maier-Hein;Matthias Eisenmann;Annika Reinke;Sinan Onogur

  • MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.

    Simon Graham;Hao Chen;Jevgenij Gamper;Qi Dou

  • Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

    Mitko Veta;Yujing J. Heng;Nikolas Stathonikos;Babak Ehteshami Bejnordi

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

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

  • A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

    Korsuk Sirinukunwattana;David R. J. Snead;Nasir M. Rajpoot

  • Micro-Net: A unified model for segmentation of various objects in microscopy images.

    Shan e Ahmed Raza;Shan e Ahmed Raza;Linda Cheung;Muhammad Shaban;Simon Graham

  • Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study

    Mohsin Bilal;Shan-e-Ahmed Raza;Ayesha Azam;Ayesha Azam;Simon Graham

  • Validation of digital pathology imaging for primary histopathological diagnosis.

    David R J Snead;Yee-Wah Tsang;Aisha Meskiri;Peter K Kimani

  • Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice.

    Richard Colling;Helen Pitman;Karin Oien;Nasir M. Rajpoot

  • A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images.

    Adnan Mujahid Khan;Hesham ElDaly;Nasir M Rajpoot

  • Automatic detection of diseased tomato plants using thermal and stereo visible light images.

    Shan-E.-Ahmed Raza;Gillian Prince;John P Clarkson;Nasir M. Rajpoot

  • Proceedings of British machine vision conference 2007

    Nasir M. Rajpoot;Abhir Bhalerao

Frequent Co-Authors

Tim Wilhelm Nattkemper
Tim Wilhelm Nattkemper Bielefeld University
Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong
Hao Chen
Hao Chen Chinese University of Hong Kong
Danail Stoyanov
Danail Stoyanov University College London
Bram van Ginneken
Bram van Ginneken Radboud University
Lena Maier-Hein
Lena Maier-Hein German Cancer Research Center
Stephen J. McKenna
Stephen J. McKenna University of Dundee
Paul J. Thornalley
Paul J. Thornalley Hamad bin Khalifa University
Klaus H. Maier-Hein
Klaus H. Maier-Hein German Cancer Research Center
Qi Dou
Qi Dou Chinese University of Hong Kong

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