Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2012
Abstract
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.
Discipline
Computer Sciences | Health Information Technology
Research Areas
Intelligent Systems and Optimization
Publication
American Medical Informatics Association Annual Symposium (AMIA)
Volume
2012
First Page
1201
Last Page
1210
ISBN
1942-597X
Publisher
AMIA
Citation
DINH, Thien Anh; SILANDER, Tomi; LIM, C. C. Tchoyoson; 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.
Available at: https://ink.library.smu.edu.sg/sis_research/2990
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540549/