Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Healthcare data mining from multi-source data

The "big data" challenge is changing the way we acquire, store, analyse, and draw conclusions from data. How we effectively and efficiently "mine" the data from possibly multiple sources and extract useful information is a critical question. Increasing research attention has been drawn to healthcare data mining, with an ultimate goal to improve the quality of care. The human body is complex and so too the data collected in treating it. Data noise that is often introduced via the collection process makes building Data Mining models a challenging task. This thesis focuses on the classification tasks of mining healthcare data, with the goal of improving the effectiveness of health risk prediction. In particular, we developed algorithms to address issues identified from real healthcare data, such as feature extraction, heterogeneity, label uncertainty, and large unlabeled data. The three main contributions of this research are as follows. First, we developed a new health index called Personal Health Index (PHI) that scores a person's health status based on the examination records of a given population. Second, we identified the key characteristics of the real datasets and issues that were associated with the data. Third, we developed classification algorithms to cope with those issues, particularly, the label uncertainty and large unlabeled data issues. This research takes one step forward towards scoring personal health based on mining increasingly large health records. Particularly, it pioneers exploring the mining of GHE data and tackles the associated challenges. It is our anticipation that in the near future, more robust data-mining-based health scoring systems will be available for healthcare professionals to understand people's health status and thus improve the quality of care