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Hayit Greenspan

Hayit Greenspan

D-Index & Metrics

Computer Science

D-Index
65
Citations
25681
World Ranking
2402
National Ranking
46

Overview

Hayit Greenspan is affiliated with Tel Aviv University in Israel and has contributed extensively to research at the intersection of medicine and computer science, with a focus on radiology and artificial intelligence applications in healthcare.

The primary fields of study for Greenspan include Medicine and Computer Science. Their main subfields of expertise cover Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Cardiology and Cardiovascular Medicine, Computer Vision and Pattern Recognition, and Pulmonary and Respiratory Medicine.

The key research topics explored by Greenspan encompass:

  • Radiomics and Machine Learning in Medical Imaging
  • COVID-19 diagnosis using AI
  • AI in cancer detection
  • Artificial Intelligence in Healthcare and Education
  • Domain Adaptation and Few-Shot Learning
  • Venous Thromboembolism Diagnosis and Management
  • Lung Cancer Diagnosis and Treatment

Greenspan has published in a variety of scientific venues, frequently contributing to:

  • arXiv (Cornell University)
  • Scientific Reports
  • npj Digital Medicine
  • 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
  • European Radiology

Some of the notable recent papers by Greenspan include:

  • "RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning," 2022, Radiology Artificial Intelligence
  • "Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis," 2020, Scientific Reports
  • "An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection," 2020, IEEE Journal of Biomedical and Health Informatics
  • "Coronavirus Detection and Analysis on Chest CT with Deep Learning," 2020, arXiv (Cornell University)
  • "AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging," 2022, Medical Physics

Hayit Greenspan has collaborated frequently with several coauthors including Noa Cahan, Jacob Goldberger, Eyal Klang, Yiftach Barash, and Eli Konen.

The researcher has also contributed to book publications, notably publishing "Multimodal Learning for Clinical Decision Support" in 2021 through Springer Science+Business Media.

Best Publications

  • Blobworld: image segmentation using expectation-maximization and its application to image querying

    C. Carson;S. Belongie;H. Greenspan;J. Malik

  • Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

    Hayit Greenspan;Bram van Ginneken;Ronald M. Summers

  • GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

    Maayan Frid-Adar;Idit Diamant;Eyal Klang;Michal Amitai

  • A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

    S. Kevin Zhou;Hayit Greenspan;Christos Davatzikos;James S. Duncan

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

    M. Jorge Cardoso;Tal Arbel;Gustavo Carneiro;Tanveer Syeda-Mahmood

  • Synthetic data augmentation using GAN for improved liver lesion classification

    Maayan Frid-Adar;Eyal Klang;Michal Amitai;Jacob Goldberger

  • Color- and texture-based image segmentation using EM and its application to content-based image retrieval

    S. Belongie;C. Carson;H. Greenspan;J. Malik

  • Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

    Ophir Gozes;Maayan Frid-Adar;Hayit Greenspan;Patrick D. Browning

  • Super-Resolution in Medical Imaging

    Hayit Greenspan

  • Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide

    Shelly Soffer;Avi Ben-Cohen;Orit Shimon;Michal Marianne Amitai

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

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

  • Region-based image querying

    C. Carson;S. Belongie;H. Greenspan;J. Malik

  • Image enhancement by nonlinear extrapolation in frequency space

    H. Greenspan;C.H. Anderson;S. Akber

  • Chest pathology detection using deep learning with non-medical training

    Yaniv Bar;Idit Diamant;Lior Wolf;Sivan Lieberman

  • Deep learning with non-medical training used for chest pathology identification

    Yaniv Bar;Idit Diamant;Lior Wolf;Hayit Greenspan

  • Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

    Aaron Carass;Snehashis Roy;Amod Jog;Jennifer L. Cuzzocreo

  • MRI inter-slice reconstruction using super-resolution

    H. Greenspan;G. Oz;N. Kiryati;S. Peled

  • Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

    H. Greenspan;A. Ruf;J. Goldberger

  • Overcomplete steerable pyramid filters and rotation invariance

    Unknown

  • An Efficient Image Similarity Measure Based on Approximations of KL-Divergence Between Two Gaussian Mixtures

    Jacob Goldberger;Shiri Gordon;Hayit Greenspan

Frequent Co-Authors

Jacob Goldberger
Jacob Goldberger Bar-Ilan University
Tanveer Syeda-Mahmood
Tanveer Syeda-Mahmood IBM (United States)
Serge Belongie
Serge Belongie University of Copenhagen
Nir Sochen
Nir Sochen Tel Aviv University
Jitendra Malik
Jitendra Malik University of California, Berkeley
Daniel L. Rubin
Daniel L. Rubin Stanford University
Anant Madabhushi
Anant Madabhushi Emory University
Bram van Ginneken
Bram van Ginneken Radboud University
Jerry L. Prince
Jerry L. Prince Johns Hopkins University
Nahum Kiryati
Nahum Kiryati Tel Aviv University

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