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

Full Record
Other Related Research

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:: 2018-01-24

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.

Copy to clipboard


                    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

Copy to clipboard


                    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.

Copy to clipboard


                    
@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         = {2018},

  month        = {1}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.7771/2327-2937.1088

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Gaze as a biometric

Conference Yoon, Hong-Jun ; Carmichael, Tandy ; Tourassi, Georgia

Two people may analyze a visual scene in two completely different ways. Our study sought to determine whether human gaze may be used to establish the identity of an individual. To accomplish this objective we investigated the gaze pattern of twelve individuals viewing different still images with different spatial relationships. Specifically, we created 5 visual dot-pattern tests to be shown on a standard computer monitor. These tests challenged the viewer s capacity to distinguish proximity, alignment, and perceptual organization. Each test included 50 images of varying difficulty (total of 250 images). Eye-tracking data were collected from each individual while takingmore »« less
Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective

Journal Article Mohamed, Aly A. ; Luo, Yahong ; Peng, Hong ; ... - Journal of Digital Imaging (Online)

Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists,more »« less
https://doi.org/10.1007/S10278-017-0022-2
MO-F-CAMPUS-I-01: Accuracy of Radiologists Interpretation of Mammographic Breast Density

Journal Article Vedantham, S ; Shi, L ; Karellas, A ; ... - Medical Physics

Purpose: Several commercial and non-commercial software and techniques are available for determining breast density from mammograms. However, where mandated by law the breast density information communicated to the subject/patient is based on radiologist’s interpretation of breast density from mammograms. Several studies have reported on the concordance among radiologists in interpreting mammographic breast density. In this work, we investigated the accuracy of radiologist’s interpretation of breast density. Methods: Volumetric breast density (VBD) determined from 134 unilateral dedicated breast CT scans from 134 subjects was considered the truth. An MQSA-qualified study radiologist with more than 20 years of breast imaging experience reviewedmore »« less
https://doi.org/10.1118/1.4925452
Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

Journal Article Tourassi, Georgia ; Voisin, Sophie ; Paquit, Vincent C ; ... - Journal of American Medical Informatics Association

Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By poolingmore »« less
https://doi.org/10.1136/amiajnl-2012-001503
Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

Journal Article Sahiner, B ; Chan, H P ; Petrick, N ; ... - IEEE Transactions on Medical Imaging

The authors investigated the classification of regions of interest (ROI`s) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI`s using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequentlymore »« less
https://doi.org/10.1109/42.538937

Similar Records