Applying deep-layered clustering to mammography image analytics

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

This paper details a methodology and preliminary results for applying a hierarchy of clustering units to mammographic image data. The identification of patients with breast cancer through the detection of microcalcifications and masses is a demanding classification problem; minimal false negatives are desired while simultaneously avoiding false positives that cause unnecessary cost to patients and health institutions. This research examines a segmented look at mammograms for computer aided detection with the goal of reliably labeling regions of interest requiring the attention of a radiologist. Classification is achieved by employing the building blocks, namely unsupervised clustering, of a deep learning architecture in tandem with a standard feed-forward neural network. Early results show promise for creating a classification engine that handles high-dimensional data with minimum engineering of image features, with a per-image patch sensitivity of 0.96 and specificity of 0.99.

Original languageEnglish
Title of host publicationProceedings of the 2010 Biomedical Science and Engineering Conference, BSEC 2010
Subtitle of host publicationBiomedical Research and Analysis in Neuroscience, BRAiN
DOIs
StatePublished - 2010
Event2010 Biomedical Science and Engineering Conference, BSEC 2010: Biomedical Research and Analysis in Neuroscience, BRAiN - Oak Ridge, TN, United States
Duration: May 25 2010May 26 2010

Publication series

NameProceedings of the 2010 Biomedical Science and Engineering Conference, BSEC 2010: Biomedical Research and Analysis in Neuroscience, BRAiN

Conference

Conference2010 Biomedical Science and Engineering Conference, BSEC 2010: Biomedical Research and Analysis in Neuroscience, BRAiN
Country/TerritoryUnited States
CityOak Ridge, TN
Period05/25/1005/26/10

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