An Automated Pathological Class Level Annotation System for Volumetric Brain Images
Conference Proceeding Article
We introduce an automated, pathological class level annotation system for medical volumetric brain images. While much of the earlier work has mainly focused on annotating regions of interest in medical images, our system does not require annotated region level training data nor assumes perfect segmentation results for the regions of interest; the time and effort needed for acquiring training data are hence significantly reduced. This capability of handling high-dimensional noisy data, however, poses additional technical challenges, since statistical estimation of models for such data is prone to over-fitting. We propose a framework that combines a regularized logistic regression method and a kernel-based discriminative method to address these problems. Regularized methods provide a flexible selection mechanism that is well-suited for high dimensional noisy data. Our experiments show promising results in classifying computer tomography images of traumatic brain injury patients into pathological classes.
Computer Sciences | Health Information Technology
Intelligent Systems and Decision Analytics
American Medical Informatics Association Annual Symposium (AMIA)
City or Country
Chicago, IL, USA
Dinh T., Silander T., Lim C., and Tze-Yun LEONG.
An Automated Pathological Class Level Annotation System for Volumetric Brain Images. (2012). American Medical Informatics Association Annual Symposium (AMIA). 2012, 1201-1210. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2990