TY - GEN
T1 - Large margin aggregation of local estimates for medical image classification
AU - Song, Yang
AU - Cai, Weidong
AU - Huang, Heng
AU - Zhou, Yun
AU - Feng, David Dagan
AU - Chen, Mei
PY - 2014
Y1 - 2014
N2 - Medical images typically exhibit complex feature space distributions due to high intra-class variation and inter-class ambiguity. Monolithic classification models are often problematic. In this study, we propose a novel Large Margin Local Estimate (LMLE) method for medical image classification. In the first step, the reference images are sub-categorized, and local estimates of the test image are computed based on the reference subcategories. In the second step, the local estimates are fused in a large margin model to derive the similarity level between the test image and the reference images, and the test image is classified accordingly. For evaluation, the LMLE method is applied to classify image patches of different interstitial lung disease (ILD) patterns on high-resolution computed tomography (HRCT) images. We demonstrate promising performance improvement over the state-of-the-art.
AB - Medical images typically exhibit complex feature space distributions due to high intra-class variation and inter-class ambiguity. Monolithic classification models are often problematic. In this study, we propose a novel Large Margin Local Estimate (LMLE) method for medical image classification. In the first step, the reference images are sub-categorized, and local estimates of the test image are computed based on the reference subcategories. In the second step, the local estimates are fused in a large margin model to derive the similarity level between the test image and the reference images, and the test image is classified accordingly. For evaluation, the LMLE method is applied to classify image patches of different interstitial lung disease (ILD) patterns on high-resolution computed tomography (HRCT) images. We demonstrate promising performance improvement over the state-of-the-art.
UR - http://www.scopus.com/inward/record.url?scp=84906980116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906980116&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10470-6_25
DO - 10.1007/978-3-319-10470-6_25
M3 - Conference contribution
AN - SCOPUS:84906980116
SN - 9783319104690
VL - 8674 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 203
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Y2 - 14 September 2014 through 18 September 2014
ER -