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Full-Text Articles in Engineering
Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc
Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc
USF Tampa Graduate Theses and Dissertations
Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …
Learning To Predict Clinical Outcomes From Soft Tissue Sarcoma Mri, Hamidreza Farhidzadeh
Learning To Predict Clinical Outcomes From Soft Tissue Sarcoma Mri, Hamidreza Farhidzadeh
USF Tampa Graduate Theses and Dissertations
Soft Tissue Sarcomas (STS) are among the most dangerous diseases, with a 50% mortality rate in the USA in 2016. Heterogeneous responses to the treatments of the same sub-type of STS as well as intra-tumor heterogeneity make the study of biopsies imprecise. Radiologists make efforts to find non-invasive approaches to gather useful and important information regarding characteristics and behaviors of STS tumors, such as aggressiveness and recurrence. Quantitative image analysis is an approach to integrate information extracted using data science, such as data mining and machine learning with biological an clinical data to assist radiologists in making the best recommendation …