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CVBIA
2005
Springer
9 years 4 months ago
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Abstract. Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a dis...
Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bi...
MICCAI
2008
Springer
10 years 12 days ago
Segmenting Brain Tumors Using Pseudo-Conditional Random Fields
Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer. This segmentation task requires classifying each voxel as eith...
Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matt...
AAAI
2008
9 years 1 months ago
Constrained Classification on Structured Data
Most standard learning algorithms, such as Logistic Regression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identically distributed...
Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner...
ISBI
2008
IEEE
9 years 5 months ago
Support vector driven Markov random fields towards DTI segmentation of the human skeletal muscle
In this paper we propose a classiļ¬cation-based method towards the segmentation of diffusion tensor images. We use Support Vector Machines to classify diffusion tensors and we ex...
Radhouène Neji, Gilles Fleury, Jean Francoi...
ICASSP
2010
IEEE
8 years 11 months ago
Language recognition using deep-structured conditional random fields
We present a novel language identification technique using our recently developed deep-structured conditional random fields (CRFs). The deep-structured CRF is a multi-layer CRF mo...
Dong Yu, Shizhen Wang, Zahi Karam, Li Deng
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