Principal-component massive-training machine-learning regression for false-positive reduction in computer-aided detection of polyps in CT colonography

Authors:
Kenji Suzuki;Jianwu Xu;Jun Zhang;Ivan Sheu
Affiliations:
Department of Radiology, The University of Chicago, Chicago, IL;Department of Radiology, The University of Chicago, Chicago, IL;Department of Radiology, The University of Chicago, Chicago, IL;Department of Radiology, The University of Chicago, Chicago, IL
Venue:
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Year:
2010

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Abstract

A massive-training artificial neural network (MTANN) has been investigated for reduction of false positives (FPs) in computer-aided detection (CAD) of lesions in medical images. The MTANN is trained with a massive number of subvolumes extracted from input volumes; hence the term "massive training". A major limitation of this technique is a long training time due to the high input dimensionality. To solve this problem, we incorporated principal-component (PC) analysis for dimension reduction into the MTANN framework, which we call a PC-MTANN. To test the PC-MTANN, we compared it with the original MTANN in FP reduction in CAD of polyps in CT colonography. With the use of the dimension reduction architecture, the time required for training was reduced substantially from 38 to 4 hours, while the original performance was maintained, i.e., a 96% sensitivity at an FP rate of 3.2 and 3.0 per patient by the original MTANN and the PC-MTANN, respectively.