PO-1004: Machine learning methods for automated OAR segmentation

Automated Walks using Machine Learning for Segmentation

This paper describes an automated algorithm for segmentation of brain structures (CSF, white matter, and gray matter) in MR images. We employ machine learning (using k -Nearest Neighbors) of features derived from k -means, Canny edge detection, and Tourist Walks to fully automate the seeding process of the Random Walker algorithm. We test our methods on the MRBrainS13 dataset, which consists of...

Combination of Machine Learning Methods for Optimum Chinese Word Segmentation

This article presents our recent work for participation in the Second International Chinese Word Segmentation Bakeoff. Our system performs two procedures: Out-ofvocabulary extraction and word segmentation. We compose three out-of-vocabulary extraction modules: Character-based tagging with different classifiers – maximum entropy, support vector machines, and conditional random fields. We also co...

Machine Learning Methods for Automated Detection of Severe Diabetic Neuropathy

Objective: The present study aimed at investigating machine learning methods for automated detection of severe diabetic neuropathy. Severe diabetic neuropathy represents a significant neurological problem in diabetes as it requires urgent intervention to reduce the risk of sudden cardiac death. Automated detection provides a tool that can be applied to clinical data and for identifying comorbid...

Machine Learning for Automated Reasoning

Bundle Methods for Machine Learning

We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other regularized risk minimization setting which leads to a convex optimization problem. SVMPerf can be shown to be a special case of our approach. In addition to the unified framework we present tight convergence bounds, w...