TY - GEN
T1 - Applying deep-layered clustering to mammography image analytics
AU - Rose, Derek C.
AU - Arel, Itamar
AU - Karnowski, Thomas P.
AU - Paquit, Vincent C.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77955648349&partnerID=8YFLogxK
U2 - 10.1109/BSEC.2010.5510799
DO - 10.1109/BSEC.2010.5510799
M3 - Conference contribution
AN - SCOPUS:77955648349
SN - 9781424467143
T3 - Proceedings of the 2010 Biomedical Science and Engineering Conference, BSEC 2010: Biomedical Research and Analysis in Neuroscience, BRAiN
BT - Proceedings of the 2010 Biomedical Science and Engineering Conference, BSEC 2010
T2 - 2010 Biomedical Science and Engineering Conference, BSEC 2010: Biomedical Research and Analysis in Neuroscience, BRAiN
Y2 - 25 May 2010 through 26 May 2010
ER -