Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

machine learning;deep learning;feature learning;image analysis;segmentation;disease diagnosis

Ehsan Hosseini-Asl

University:University of Louisville

Department:Electrical and Computer Engineering

关键词: machine learning;    deep learning;    feature learning;    image analysis;    segmentation;    disease diagnosis;   

Others  :  https://ir.library.louisville.edu/cgi/viewcontent.cgi?article=3368&context=etd

美国|英语

来源: The Universite of Louisville's Institutional Repository

【 摘 要 】

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer's disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction.