Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Quality assurance and training procedures for computerâ aided detection and diagnosis systems in clinical usea)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134756/1/mp7642.pd

Social support and social structure

The burgeoning study of social support in relation to social stress and health would benefit from increased attention to issues of social structure. Three aspects of social relationships, all often referred to as social support, must be more clearly distinguished—(1) their existence or quantity (i.e., social integration), (2) their formal structure (i.e., social networks), and (3) their functional or behavioral content (i.e., the most precise meaning of “social support”)—and the causal relationships between the structure of social relationships (social integration and networks) and their functional content (social support) must be more clearly understood. Research and theory are needed on the determinants of social integration, networks, and support as well as their consequences for stress and health. Among potential determinants, macrosocial structures and processes particularly merit attention.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45658/1/11206_2005_Article_BF01107897.pd

Application of Ligninolytic Enzymes in the Production of Biofuels from Cotton Wastes

The application of ligninolytic fungi and enzymes is an option to overcome the issues related with the production of biofuels using cotton wastes. In this dissertation, the ligninolytic fungus and enzymes were evaluated as pretreatment for the biochemical conversion of Cotton Gin Trash (CGT) in ethanol and as a treatment for the transformation of cotton wastes biochar in other substances. In biochemical conversion, seven combinations of three pretreatments (ultrasonication, liquid hot water and ligninolytic enzymes) were evaluated on CGT. The best results were achieved by the sequential combination of ultrasonication, hot water, and ligninolytic enzymes with an improvement of 10% in ethanol yield. To improve these results, alkaline-ultrasonication was evaluated. Additionally, Fourier Transform Infrared (FT-IR) and principal component analysis (PCA) were employed as fast methodology to identify structural differences in the biomass. The combination of ultrasonication-alkali hydrolysis, hot liquid water, and ligninolytic enzymes using 15% of NaOH improved 35% ethanol yield compared with the original treatment. Additionally, FT-IR and PCA identified modifications in the biomass structure after different types of pretreatments and conditions. In thermal conversion, this study evaluated the biodepolymerization of cotton wastes biochar using chemical and biological treatments. The chemical depolymerization evaluated three chemical agents (KMnO4, H2SO4, and NaOH), with three concentrations and two environmental conditions. The sulfuric acid treatments performed the largest transformations of the biochar solid phase; whereas, the KMnO4 treatments achieved the largest depolymerizations. The compounds released into the liquid phase were correlated with fulvic and humic acids and silicon compounds. The biological depolymerization utilized four ligninolytic fungi Phanerochaete chrysosporium, Ceriporiopsis subvermispora, Postia placenta, and Bjerkandera adusta. The greatest depolymerization was obtained by C. subvermispora. The depolymerization kinetics of C. subvermispora evidenced the production of laccase and manganese peroxidase and a correlation between depolymerization and production of ligninolytic enzymes. The modifications obtained in the liquid and solid phases showed the production of humic and fulvic acids from the cultures with C. subvermispora. The results of this research are the initial steps for the development of new processes using the ligninolytic fungus and their enzymes for the production of biofuels from cotton wastes

Optimizing area under the ROC curve using semi-supervised learning

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.(