Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/70395
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Type: Conference paper
Title: Incremental on-line semi-supervised learning for segmenting the left ventricle of the heart from ultrasound data
Author: Carneiro, G.
Nascimento, J.
Citation: 2011 IEEE International Conference on Computer Vision, 2011: pp.1700-1707
Publisher: IEEE
Publisher Place: 345 E 47TH ST, NEW YORK, NY 10017 USA
Issue Date: 2011
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781457711015
ISSN: 1550-5499
Conference Name: International Conference on Computer Vision (13th : 2011 : Barcelona, Spain)
Statement of
Responsibility: 
Gustavo Carneiro, Jacinto C. Nascimento
Abstract: Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of the heart from ultrasound data. The main vulnerability of these models resides in the need of large manually annotated training sets for the parameter estimation procedure. The issue is that these training sets need to be annotated by clinicians, which makes this training set acquisition process quite expensive. Therefore, reducing the dependence on large training sets is important for a more extensive exploration of statistical models in the LV segmentation problem. In this paper, we present a novel incremental on-line semi-supervised learning model that reduces the need of large training sets for estimating the parameters of statistical models. Compared to other semi-supervised techniques, our method yields an on-line incremental re-training and segmentation instead of the off-line incremental re-training and segmentation more commonly found in the literature. Another innovation of our approach is that we use a statistical model based on deep learning architectures, which are easily adapted to this on-line incremental learning framework. We show that our fully automatic LV segmentation method achieves state-of-the-art accuracy with training sets containing less than twenty annotated images.
Rights: Copyright © 2011 by IEEE.
DOI: 10.1109/ICCV.2011.6126433
Description (link): http://www.iccv2011.org/
Published version: http://dx.doi.org/10.1109/iccv.2011.6126433
Appears in Collections:Aurora harvest
Computer Science publications

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