Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy; Morin-Ducote, Garnetta; Tourassi, Georgia

doi:10.7771/2327-2937.1088

Title: Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

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Abstract

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.

Authors:

^[1];

^[1]; Hudson, Kathy ^[2]; Morin-Ducote, Garnetta ^[2];

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biomedical Sciences, Engineering, and Computing Group Health Data Sciences Institute
University of Tennessee Graduate School of Medicine, Knoxville, TN (United States). Department of Radiology

Publication Date:: Wed Jan 24 00:00:00 EST 2018

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1422583

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Human Performance in Extreme Environments

Additional Journal Information:: Journal Volume: 14; Journal Issue: 1; Journal ID: ISSN 2327-2937

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; 62 RADIOLOGY AND NUCLEAR MEDICINE; convolutional neural networks; deep learning; eye tracking; gaze velocity

Citation Formats


                    Yoon, Hong-Jun, Alamudun, Folami T., Hudson, Kathy, Morin-Ducote, Garnetta, and Tourassi, Georgia. Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists.  United States: N. p., 2018. 
Web.  doi:10.7771/2327-2937.1088.

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                    Yoon, Hong-Jun, Alamudun, Folami T., Hudson, Kathy, Morin-Ducote, Garnetta, & Tourassi, Georgia. Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists.  United States.  https://doi.org/10.7771/2327-2937.1088

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                    Yoon, Hong-Jun, Alamudun, Folami T., Hudson, Kathy, Morin-Ducote, Garnetta, and Tourassi, Georgia. Wed .  
"Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists".  United States.  https://doi.org/10.7771/2327-2937.1088.  https://www.osti.gov/servlets/purl/1422583.

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@article{osti_1422583,

  title        = {Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists},

  author       = {Yoon, Hong-Jun and Alamudun, Folami T. and Hudson, Kathy and Morin-Ducote, Garnetta and Tourassi, Georgia},

  abstractNote = {Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.},

  doi          = {10.7771/2327-2937.1088},

  journal      = {Journal of Human Performance in Extreme Environments},

  number       = 1,

  volume       = 14,

  place        = {United States},

  year         = {Wed Jan 24 00:00:00 EST 2018},

  month        = {Wed Jan 24 00:00:00 EST 2018}

}

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