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Computer Science

D-Index
59
Citations
22121
World Ranking
3341
National Ranking
201

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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