Back to articles
Articles
Volume: 31 | Article ID: art00014
Image
Do different radiologists perceive medical images the same way? Some insights from Representational Similarity Analysis
  DOI :  10.2352/ISSN.2470-1173.2019.12.HVEI-225  Published OnlineJanuary 2019
Abstract

Characterizing what experts perceive in medical images is a difficult problem, both because doing so requires somehow characterizing the internal mental representations of the observer, and because the underlying diagnostic information tends to be abstract and not readily describable in terms of well-defined image features. Representational Similarity Analysis (RSA) is a method originally developed in mathematical psychology that provides a theoretically sound and quantitative framework for measuring the mental representations of visual images in human observers. Here we used RSA to measure the extent to which the same underlying set of mammograms elicit similar mental representations in different practicing radiologists (N = 26). We found that the internal representations were statistically indistinguishable across different radiologists (p > 0.05). Moreover, the mental representations significantly parallel the diagnostic information in the images (p <0.05 for each subject), indicating that various radiologists perceived the same set of diagnostic information in the underlying images. Together, these results indicate that medical images elicit similar mental representations in different radiologists.

Subject Areas :
Views 16
Downloads 2
 articleview.views 16
 articleview.downloads 2
  Cite this article 

Jay Hegdé, Evgeniy Bart, "Do different radiologists perceive medical images the same way? Some insights from Representational Similarity Analysisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2019,  pp 225-1 - 225-6,  https://doi.org/10.2352/ISSN.2470-1173.2019.12.HVEI-225

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2019
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA