Statistical Curve Analysis: Developing Methods and Expanding Knowledge in Health

The analysis of curves can be claimed to be the core of most scientific ventures. In this dissertation, we focus on the statistical aspect of this type of analysis. Here, the curves originate from health and food-related areas and include improvements in blood glucose measurements, classification of moles, measurements of parameters during liver transplants in pigs, and data from the monitoring of the quality of fish. More specifically, the statistical curve analysis consists of several perspectives were all have some kind of in- trinsic comparison effort. However, the main approaches in these studies are related to regression and the problem of finding suitable critical regions. The regression part consists of robust nonlinear regression and linear mixed models while the critical regions are found through classification and hypothesis testing in scale-space. By improving the critical decision boundaries through e.g. the Bonferroni correction of scale-space maps in Paper I, and developing features to improve decisions regarding the classification of moles in Paper II, we were able to obtain high sensitivity and specificity in the developed systems. Re- gression was an integral part of the classification effort in Paper II, the improvement of blood glucose measurements in Paper III, and the statistical analysis of parameters measured during liver transplantation in pigs in Paper IV. Paper I is focused on maximizing sensitivity and specificity when detecting a significant change in the data. Here as in Paper II hyperspectral images are the source of data. The developed method produces a scale-space, where significant changes can be detected. Paper II aims to maximize sensitivity, specificity, and precision in the classification of moles. This is accomplished through curves from subimages obtained from each channel of the hyperspectral images. These curves show characteristic features from three important classes of moles. By using these features through the regression of these curves, we accomplish high sensitivity, specificity, and precision in the classification pursuit. In Paper III, we introduce a novel method for improving blood glucose estimation from continuous glucose measurements by using deconvolution. First, regression is used to estimate the parameters in the convolution kernel. Thereafter this response function was deconvolved through regression. In this way, we can estimate blood glucose from subcutaneous measurements. This gives a new method for controlling blood glucose levels which is of great importance for type 1 diabetes patients during and after exercise to avoid hypoglycemia. Testing two different methods in liver transplantation of pigs, where the statistical analysis of curves was done through the application of linear mixed models, is the focus of Paper IV. An important output of this work is that the two treatments can be statistically distinguished through the use of linear mixed models

Leveraging Computer Vision for Applications in Biomedicine and Geoscience

Skin cancer is one of the most common types of cancer and is usually classified as either non-melanoma and melanoma skin cancer. Melanoma skin cancer accounts for about half of all skin cancer-related deaths. The 5-year survival rate is 99% when the cancer is detected early but drops to 25% once it becomes metastatic. In other words, the key to preventing death is early detection. Foraminifera are microscopic single-celled organisms that exist in marine environments and are classified as living a benthic or planktic lifestyle. In total, roughly 50,000 species are known to have existed, of which about 9,000 are still living today. Foraminifera are important proxies for reconstructing past ocean and climate conditions and as bio-indicators of anthropogenic pollution. Since the 1800s, the identification and counting of foraminifera have been performed manually. The process is resource-intensive. In this dissertation, we leverage recent advances in computer vision, driven by breakthroughs in deep learning methodologies and scale-space theory, to make progress towards both early detection of melanoma skin cancer and automation of the identification and counting of microscopic foraminifera. First, we investigate the use of hyperspectral images in skin cancer detection by performing a critical review of relevant, peer-reviewed research. Second, we present a novel scale-space methodology for detecting changes in hyperspectral images. Third, we develop a deep learning model for classifying microscopic foraminifera. Finally, we present a deep learning model for instance segmentation of microscopic foraminifera. The works presented in this dissertation are valuable contributions in the fields of biomedicine and geoscience, more specifically, towards the challenges of early detection of melanoma skin cancer and automation of the identification, counting, and picking of microscopic foraminifera

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual lack of rationale, understanding and transparency for how they work and how to interpret information and results from them. For example, a user must select the kernel, or similarity function, to be used, and there are many kernels to choose from but little to no useful guidance on choosing one. The primary goal of this thesis is to create accurate, transparent and interpretable kernels with rationale to select them for classification in health care using SVM—and to do so within a theoretical framework that advances rationale, understanding and transparency for kernel/model selection with atomic data types. The kernels and framework necessarily co-exist. The secondary goal of this thesis is to quantitatively measure model interpretability for kernel/model selection and identify the types of interpretable information which are available from different models for interpretation. Testing my framework and transparent kernels with empirical data I achieve classification accuracy that is better than or equivalent to the Gaussian RBF kernels. I also validate some of the model interpretability measures I propose