Technical Program

Paper Detail

Paper:	SU-AM-PS4.3
Session:	Image Segmentation, Retrieval and Analysis
Time:	Sunday, April 9, 10:50 - 12:10
Presentation:	Poster
Title:	Learning of Perceptual Similarity from Expert Readers for Mammogram Retrieval
Authors:	Liyang Wei; Illinois Institute of Technology
	Yongyi Yang; Illinois Institute of Technology
	Robert.M. Nishikawa; University of Chicago
	Miles N. Wernick; Illinois Institute of Technology
Abstract:	Image retrieval relies critically on the similarity measure used to compare a query image to a target image in a database. In this work, we explore a similarity measure for mammogram retrieval based on supervised learning from expert readers. This approach is evaluated using data collected from an observer study with a set of clinical mammograms. Our results demonstrate that the proposed machine learning approach can be used to model the notion of similarity as judged by expert readers in their interpretation of mammogram images and that it can outperform alternative similarity measures derived from unsupervised learning.

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