EfficientL1regularized logistic regression
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Shape-Based Classification of 3D Head Data
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Efficient symbolic signatures for classifying craniosynostosis skull deformities
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Skull retrieval for craniosynostosis using sparse logistic regression models
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Craniosynostosis is the premature fusion of the bones of the calvaria resulting in abnormal skull shapes that can be associated with increased intracranial pressure. The goal of this work is to analyze the various 3D skull shapes that manifest in isolated single suture craniosynostosis. A logistic regression is used to identify different types of synostosis and quantify the differences. Due to the high-dimensionality of the feature data, a sophisticated feature selection technique is required to avoid overfitting and to improve the classification accuracy on the unseen data. In addition, feature selection allows the identification of surface areas that contribute to the major skull deformations that characterize isolated synostosis. We applied three sparse feature selection methods: L1 regularization (lasso [9]), fused lasso ([10]) and a novel regularization method we have developed called the clustering lasso (cLasso). L1 regularized logistic regression locates important surface points, and the fused lasso groups these points into regions. The cLasso was designed to assign similar weights to groups of correlated shape features. Experimental results indicated that the regularized logistic regression models achieve a significantly lower misclassification rate than unregularized logistic regression.