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Computer Science

D-Index
77
Citations
26881
World Ranking
1254
National Ranking
667

Overview

Daniel L. Rubin is affiliated with Stanford University in the United States. Their research spans multiple disciplines within medicine and computer science, with a significant focus on applications of artificial intelligence in medical imaging and healthcare.

The scientist's primary fields of study include:

  • Medicine
  • Computer Science

Within these broader fields, their main subfields of study comprise:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Oncology
  • Ophthalmology
  • Health Informatics

Rubin's work addresses several key topics, including:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Machine Learning in Healthcare
  • COVID-19 diagnosis using AI
  • Artificial Intelligence in Healthcare and Education
  • Retinal Imaging and Analysis
  • Retinal Diseases and Treatments

The scientist has contributed to a body of research published in notable venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Scientific Reports
  • Radiology Artificial Intelligence
  • Journal of Clinical Oncology

Recent scientific papers authored or co-authored by Daniel L. Rubin include:

  • Preparing Medical Imaging Data for Machine Learning, 2020, Radiology
  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms, 2020, JAMA Network Open
  • Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study, 2020, The Lancet Oncology
  • A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions, 2022, Scientific Data
  • Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations, 2020, Journal of the American College of Radiology

Frequent collaborators in Rubin's research include:

  • Imon Banerjee
  • Liangqiong Qu
  • Siyi Tang
  • Rikiya Yamashita
  • Christopher Lee-Messer

Best Publications

  • Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

    Kaustubh Supekar;Vinod Menon;Daniel J Rubin;Mark A. Musen

  • Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

    Zeynettin Akkus;Alfiia Galimzianova;Assaf Hoogi;Daniel L. Rubin

  • BioPortal: ontologies and integrated data resources at the click of a mouse

    Natalya Fridman Noy;Nigam H. Shah;Patricia L. Whetzel;Benjamin Dai

  • Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

    Kun-Hsing Yu;Ce Zhang;Gerald J. Berry;Russ B. Altman

  • Preparing Medical Imaging Data for Machine Learning.

    Martin J. Willemink;Wojciech A. Koszek;Cailin Hardell;Jie Wu

  • A curated mammography data set for use in computer-aided detection and diagnosis research.

    Rebecca Sawyer Lee;Francisco Gimenez;Assaf Hoogi;Kanae Kawai Miyake

  • PharmGKB: the Pharmacogenetics Knowledge Base.

    Micheal Hewett;Diane E. Oliver;Daniel L. Rubin;Katrina L. Easton

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

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

  • Integrating genotype and phenotype information: an overview of the PharmGKB project

    T. E. Klein;Jeffrey T Chang;M. K. Cho;K. L. Easton

  • MR imaging predictors of molecular profile and survival: Multi-institutional study of the TCGA glioblastoma data set

    David A. Gutman;Lee A.D. Cooper;Scott N. Hwang;Chad A. Holder

  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

    Thomas Schaffter;Diana S. M. Buist;Christoph I. Lee;Yaroslav Nikulin

  • Biomedical ontologies: a functional perspective

    Daniel L. Rubin;Nigam Shah;Natalya Fridman Noy

  • Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.

    Rikiya Yamashita;Jin Long;Teri Longacre;Lan Peng

  • Distributed deep learning networks among institutions for medical imaging.

    Ken Chang;Niranjan Balachandar;Carson K. Lam;Darvin Yi

  • Differential Data Augmentation Techniques for Medical Imaging Classification Tasks.

    Zeshan Hussain;Francisco Gimenez;Darvin Yi;Daniel L. Rubin

  • Deep Learning in Neuroradiology

    G. Zaharchuk;E. Gong;M. Wintermark;D. Rubin

  • 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

  • Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

    Mehmet Günhan Ertosun;Daniel L. Rubin

  • Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

    Imon Banerjee;Yuan Ling;Matthew C. Chen;Sadid A. Hasan

  • Robust noise region-based active contour model via local similarity factor for image segmentation

    Sijie Niu;Sijie Niu;Sijie Niu;Qiang Chen;Luis de Sisternes;Zexuan Ji

  • BioPortal: Ontologies and Integrated Data Resources at the Click of a Mouse

    Patricia L. Whetzel;Nigam H. Shah;Natalya F. Noy;Benjamin Dai

Frequent Co-Authors

Sandy Napel
Sandy Napel Stanford University
Mark A. Musen
Mark A. Musen Stanford University
Adrien Depeursinge
Adrien Depeursinge University of Applied Sciences and Arts Western Switzerland
Russ B. Altman
Russ B. Altman Stanford University
Christopher Ré
Christopher Ré Stanford University
Jayashree Kalpathy-Cramer
Jayashree Kalpathy-Cramer Harvard University
Nigam H. Shah
Nigam H. Shah Stanford University
Natalya F. Noy
Natalya F. Noy Google (United States)
Jessica A. Turner
Jessica A. Turner The Ohio State University
Teri E. Klein
Teri E. Klein Stanford University

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