@inproceedings{da6f418209ec48eab15b7b5ff7a3db9f,
title = "Simplified labeling process for medical image segmentation",
abstract = "Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms.",
author = "Mingchen Gao and Junzhou Huang and Xiaolei Huang and Shaoting Zhang and Metaxas, {Dimitris N.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 ; Conference date: 05-10-2012 Through 05-10-2012",
year = "2012",
doi = "10.1007/978-3-642-33418-4_48",
language = "English (US)",
isbn = "9783642334177",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "387--394",
editor = "Le Lu and Antonio Criminisi and Nicholas Ayache and Herv{\'e} Delingette and Menze, {Bjoern H.} and Menze, {Bjoern H.} and Georg Langs and Georg Langs and Albert Montillo and Zhuowen Tu and Polina Golland and Kensaku Mori",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings",
address = "Germany",
}