TY - GEN
T1 - Lossless Online Ensemble Learning (LOEL) and its application to subcortical segmentation
AU - Morra, Jonathan H.
AU - Tu, Zhuowen
AU - Toga, Arthur W.
AU - Thompson, Paul M.
N1 - Funding Information:
This work was funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 RR021813 entitled Center for Computational Biology (CCB), the National Institute for Biomedical Imaging and Bioengineering, the National Center for Research Resources, National Institute on Aging, the National Library of Medicine, and the National Institute for Child Health and Development (EB01651, RR019771, HD050735, AG016570, LM05639).
PY - 2009
Y1 - 2009
N2 - In this paper, we study the classification problem in the situation where large volumes of training data become available sequentially (online learning). In medical imaging, this is typical, e.g., a 3D brain MRI dataset may be gradually collected from a patient population, and not all of the data is available when the analysis begins. First, we describe two common ensemble learning algorithms, AdaBoost and bagging, and their corresponding online learning versions. We then show why each is ineffective for segmenting a gradually increasing set of medical images. Instead, we introduce a new ensemble learning algorithm, termed Lossless Online Ensemble Learning (LOEL). This algorithm is lossless in the online case, compared to its batch mode. LOEL outperformed online-AdaBoost and online-bagging when validated on a standardized dataset; it also performed better when used to segment the hippocampus from brain MRI scans of patients with Alzheimer's Disease and matched healthy subjects. Among those tested, LOEL largely outperformed the alternative online learning algorithms and gave excellent error metrics that were consistent between the online and offline case; it also accurately distinguished AD subjects from healthy controls based on automated measures of hippocampal volume.
AB - In this paper, we study the classification problem in the situation where large volumes of training data become available sequentially (online learning). In medical imaging, this is typical, e.g., a 3D brain MRI dataset may be gradually collected from a patient population, and not all of the data is available when the analysis begins. First, we describe two common ensemble learning algorithms, AdaBoost and bagging, and their corresponding online learning versions. We then show why each is ineffective for segmenting a gradually increasing set of medical images. Instead, we introduce a new ensemble learning algorithm, termed Lossless Online Ensemble Learning (LOEL). This algorithm is lossless in the online case, compared to its batch mode. LOEL outperformed online-AdaBoost and online-bagging when validated on a standardized dataset; it also performed better when used to segment the hippocampus from brain MRI scans of patients with Alzheimer's Disease and matched healthy subjects. Among those tested, LOEL largely outperformed the alternative online learning algorithms and gave excellent error metrics that were consistent between the online and offline case; it also accurately distinguished AD subjects from healthy controls based on automated measures of hippocampal volume.
UR - https://www.scopus.com/pages/publications/84883835782
U2 - 10.1007/978-3-642-04271-3_53
DO - 10.1007/978-3-642-04271-3_53
M3 - Conference contribution
C2 - 20426141
AN - SCOPUS:84883835782
SN - 3642042708
SN - 9783642042706
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 432
EP - 440
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2009 - 12th International Conference, Proceedings
T2 - 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Y2 - 20 September 2009 through 24 September 2009
ER -