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Proceedings Paper

Predictive modeling of human perception subjectivity: feasibility study of mammographic lesion similarity
Author(s): Songhua Xu; Kathleen Hudson; Yong Bradley; Brian J. Daley; Katherine Frederick-Dyer; Georgia Tourassi
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Paper Abstract

The majority of clinical content-based image retrieval (CBIR) studies disregard human perception subjectivity, aiming to duplicate the consensus expert assessment of the visual similarity on example cases. The purpose of our study is twofold: i) discern better the extent of human perception subjectivity when assessing the visual similarity of two images with similar semantic content, and (ii) explore the feasibility of personalized predictive modeling of visual similarity. We conducted a human observer study in which five observers of various expertise were shown ninety-nine triplets of mammographic masses with similar BI-RADS descriptors and were asked to select the two masses with the highest visual relevance. Pairwise agreement ranged between poor and fair among the five observers, as assessed by the kappa statistic. The observers' self-consistency rate was remarkably low, based on repeated questions where either the orientation or the presentation order of a mass was changed. Various machine learning algorithms were explored to determine whether they can predict each observer's personalized selection using textural features. Many algorithms performed with accuracy that exceeded each observer's self-consistency rate, as determined using a cross-validation scheme. This accuracy was statistically significantly higher than would be expected by chance alone (two-tailed p-value ranged between 0.001 and 0.01 for all five personalized models). The study confirmed that human perception subjectivity should be taken into account when developing CBIR-based medical applications.

Paper Details

Date Published: 28 February 2012
PDF: 9 pages
Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83180M (28 February 2012); doi: 10.1117/12.913639
Show Author Affiliations
Songhua Xu, Oak Ridge National Lab. (United States)
Kathleen Hudson, The Univ. of Tennessee Medical Ctr. at Knoxville (United States)
Yong Bradley, The Univ. of Tennessee Medical Ctr. at Knoxville (United States)
Brian J. Daley, The Univ. of Tennessee Medical Ctr. at Knoxville (United States)
Katherine Frederick-Dyer, The Univ. of Tennessee Medical Ctr. at Knoxville (United States)
Georgia Tourassi, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 8318:
Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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