World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
68
Citations
23562
World Ranking
2053
National Ranking
1037

Overview

Sandy Napel is affiliated with Stanford University in the United States and specializes in Medicine, with a significant focus on Radiology, Nuclear Medicine and Imaging. Their research portfolio spans several subfields including Pulmonary and Respiratory Medicine, Artificial Intelligence, Health Informatics, and Biomedical Engineering.

Their primary topics of work include:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Artificial Intelligence in Healthcare and Education
  • Lung Cancer Diagnosis and Treatment
  • Advanced X-ray and CT Imaging
  • Sarcoma Diagnosis and Treatment
  • COVID-19 diagnosis using AI

Among Sandy Napel's notable recent papers are:

  • "The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping" (2020) published in Radiology
  • "The Medical Segmentation Decathlon" (2022) published in Nature Communications
  • "Artificial intelligence and machine learning in cancer imaging" (2022) published in Communications Medicine
  • "FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare" (2025) published in BMJ
  • "A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets" (2020) published in Nature Machine Intelligence

Frequent co-authors collaborating with Sandy Napel include:

  • Sarah A. Mattonen
  • Olivier Gevaert
  • Spyridon Bakas
  • Keyvan Farahani
  • M. Jorge Cardoso

The scientist's work has appeared frequently in several publication venues such as:

  • Journal of Medical Imaging
  • arXiv (Cornell University)
  • Tomography
  • Journal of Clinical Oncology
  • Radiology

Sandy Napel's research involves interdisciplinary efforts integrating advanced imaging techniques and artificial intelligence to improve diagnostic and prognostic capabilities, particularly in the context of cancer and respiratory diseases. Their work on radiomics and machine learning contributes to the development of standardized quantitative methods for high-throughput image analysis.

Best Publications

  • The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

    Alex Zwanenburg;Alex Zwanenburg;Martin Vallières;Mahmoud A. Abdalah;Hugo J. W. L. Aerts;Hugo J. W. L. Aerts

  • Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

    Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky

  • Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques

    West J;Fitzpatrick Jm;Wang My;Dawant Bm

  • The Medical Segmentation Decathlon

    Michela Antonelli;Annika Reinke;Spyridon Bakas;Keyvan Farahani

  • A large annotated medical image dataset for the development and evaluation of segmentation algorithms

    Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello

  • Perspective volume rendering of CT and MR images: applications for endoscopic imaging.

    G D Rubin;C F Beaulieu;V Argiro;H Ringl

  • Content-Based Image Retrieval in Radiology: Current Status and Future Directions

    Ceyhun Burak Akgül;Daniel L. Rubin;Sandy Napel;Christopher F. Beaulieu

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

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

  • Comparison and evaluation of retrospective intermodality image registration techniques

    Jay B. West;J. Michael Fitzpatrick;Matthew Yang Wang;Benoit M. Dawant

  • Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

    Olivier Gevaert;Lex A. Mitchell;Achal S. Achrol;Jiajing Xu

  • Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

    D.S. Paik;C.F. Beaulieu;G.D. Rubin;B. Acar

  • Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection.

    Geoffrey D Rubin;John K Lyo;David S Paik;Anthony J Sherbondy

  • Characterization of spatial distortion in magnetic resonance imaging and its implications for stereotactic surgery.

    Thilaka S. Sumanaweera;John R. Adler;Sandy Napel;Gary H. Glover

  • Automated polyp detector for CT colonography: feasibility study.

    Summers Rm;Beaulieu Cf;Pusanik Lm;Malley Jd

  • Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

    Haruka Itakura;Achal S. Achrol;Lex A. Mitchell;Joshua J. Loya

  • A radiogenomic dataset of non-small cell lung cancer

    Shaimaa Bakr;Olivier Gevaert;Sebastian Echegaray;Kelsey Ayers

  • Computed tomographic angiography: historical perspective and new state-of the-art using multi detector-row helical computed tomography

    G D Rubin;M C Shiau;A J Schmidt;D Fleischmann

  • Adaptive border marching algorithm: Automatic lung segmentation on chest CT images

    Jiantao Pu;Justus E. Roos;Chin A. Yi;Sandy Napel

  • Detection of ureteral calculi in patients with suspected renal colic: value of reformatted noncontrast helical CT.

    F G Sommer;R B Jeffrey;G D Rubin;S Napel

  • Automated flight path planning for virtual endoscopy

    David S. Paik;Christopher F. Beaulieu;R. Brooke Jeffrey;Geoffrey D. Rubin

  • A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography

    S.B. Gokturk;C. Tomasi;B. Acar;C.F. Beaulieu

  • Phase unwrapping of MR phase images using Poisson equation

    S. Moon-Ho Song;S. Napel;N.J. Pelc;G.H. Glover

Frequent Co-Authors

Daniel L. Rubin
Daniel L. Rubin Stanford University
Gary H. Glover
Gary H. Glover Stanford University
Salih Burak Gokturk
Salih Burak Gokturk Stanford University
Carlo Tomasi
Carlo Tomasi Duke University
Dmitry B. Goldgof
Dmitry B. Goldgof University of South Florida
Jayashree Kalpathy-Cramer
Jayashree Kalpathy-Cramer Harvard University
Norbert J. Pelc
Norbert J. Pelc Stanford University
Lubomir M. Hadjiiski
Lubomir M. Hadjiiski University of Michigan–Ann Arbor
Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Hayit Greenspan
Hayit Greenspan Tel Aviv University

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