Nonlinear Parametric and Neural Network Modelling for Medical Image Classification

System identification and artificial neural networks (ANN) are families of algorithms used in systems engineering and machine learning respectively that use structure detection and learning strategies to build models of complex systems by taking advantage of input-output type data. These models play an essential role in science and engineering because they fill the gap in those cases where we know the input-output behaviour of a system, but there is not a mathematical model to understand and predict its changes in future or even prevent threats. In this context, the nonlinear approximation of systems is nowadays very popular since it better describes complex instances. On the other hand, digital image processing is an area of systems engineering that is expanding the analysis dimension level in a variety of real-life problems while it is becoming more attractive and affordable over time. Medicine has made the most of it by supporting important human decision-making processes through computer-aided diagnosis (CAD) systems. This thesis presents three different frameworks for breast cancer detection, with approaches ranging from nonlinear system identification, nonlinear system identification coupled with simple neural networks, to multilayer neural networks. In particular, the nonlinear system identification approaches termed the Nonlinear AutoRegressive with eXogenous inputs (NARX) model and the MultiScales Radial Basis Function (MSRBF) neural networks appear for the first time in image processing. Along with the above contributions takes place the presentation of the Multilayer-Fuzzy Extreme Learning Machine (ML-FELM) neural network for faster training and more accurate image classification. A central research aim is to take advantage of nonlinear system identification and multilayer neural networks to enhance the feature extraction process, while the classification in CAD systems is bolstered. In the case of multilayer neural networks, the extraction is carried throughout stacked autoencoders, a bottleneck network architecture that promotes a data transformation between layers. In the case of nonlinear system identification, the goal is to add flexible models capable of capturing distinctive features from digital images that might be shortly recognised by simpler approaches. The purpose of detecting nonlinearities in digital images is complementary to that of linear models since the goal is to extract features in greater depth, in which both linear and nonlinear elements can be captured. This aim is relevant because, accordingly to previous work cited in the first chapter, not all spatial relationships existing in digital images can be explained appropriately with linear dependencies. Experimental results show that the methodologies based on system identification produced reliable images models with customised mathematical structure. The models came to include nonlinearities in different proportions, depending upon the case under examination. The information about nonlinearity and model structure was used as part of the whole image model. It was found that, in some instances, the models from different clinical classes in the breast cancer detection problem presented a particular structure. For example, NARX models of the malignant class showed higher non-linearity percentage and depended more on exogenous inputs compared to other classes. Regarding classification performance, comparisons of the three new CAD systems with existing methods had variable results. As for the NARX model, its performance was superior in three cases but was overcame in two. However, the comparison must be taken with caution since different databases were used. The MSRBF model was better in 5 out of 6 cases and had superior specificity in all instances, overcoming in 3.5% the closest model in this line. The ML-FELM model was the best in 6 out of 6 cases, although it was defeated in accuracy by 0.6% in one case and specificity in 0.22% in another one

Bayesian analytical approaches for metabolomics : a novel method for molecular structure-informed metabolite interaction modeling, a novel diagnostic model for differentiating myocardial infarction type, and approaches for compound identification given mass spectrometry data.

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, which we define as the testing of hypotheses that are more complex than single metabolite hypothesis tests. This methodology utilizes informative priors that are generated via the analysis of molecular structure similarity to enable the estimation of metabolite interactomes (or probabilistic models) which are organism-, sample media-, and condition-specific as well as comprehensive; and that can serve as reference models for studying perturbations in metabolic systems. After discussing the development of our methodology, we present an evaluation of its performance conducted using simulation studies, and we use the methodology for estimating a plasma metabolite interactome for stable heart disease. This interactome may serve as a reference model for evaluating systems-level changes that occur with acute disease events such as myocardial infarction (MI) or unstable angina. In the second part of this work, we present the challenge of developing diagnostic classification models which utilize metabolite abundances and that do not overfit relatively small sample sizes, especially given the high dimensionality of metabolite data acquired using platforms such as liquid chromatography-mass spectrometry. We use a Bayesian methodology for estimating a multinomial logistic regression classifier for the detection and discrimination of the subtype of acute myocardial infarction utilizing metabolite abundance data quantified from blood plasma. As heart disease is the leading cause of global mortality, a blood-based and non-invasive diagnostic test that could differentiate between MI types at the time of the event would have great utility. In the final part of this dissertation we review Bayesian approaches for compound identification in metabolomics experiments that utilize liquid chromatography-mass spectrometry which remains a challenging problem

PREDICTION OF RESPIRATORY MOTION

Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier

Cancer Outcome Prediction with Multiform Medical Data using Deep Learning

This thesis illustrated the work done for my PhD project, which aims to develop personalised cancer outcome prediction models using various types of medical data. A predictive modelling workflow that can analyse data with different forms and generate comprehensive outcome prediction was designed and implemented on a variety of datasets. The model development was accompanied by applying deep learning techniques for multivariate survival analysis, medical image analysis and sequential data processing. The modelling workflow was applied to three different tasks: 1. Deep learning models were developed for estimating the progression probability of patients with colorectal cancer after resection and after different lines of chemotherapy, which got significantly better predictive performance than the Cox regression models. Besides, CT-based models were developed for predicting the tumour local response after chemotherapy of patients with lung metastasis, which got an AUC of 0. 769 on disease progression detection and 0.794 on treatment response classification. 2. Deep learning models were developed for predicting the survival state of patients with non-small cell lung cancer after radiotherapy using CT scans, dose distribution and disease and treatment variables. The eventual model obtained by ensemble voting got an AUC of 0.678, which is significantly higher than the score achieved by the radiomics model (0.633). 3. Deep-learning-aided approaches were used for estimating the progression risk for patients with solitary fibrous tumours using digital pathology slides. The deep learning architecture was able to optimise the WHO risk assessment model using automatically identified levels of mitotic activity. Compared to manual counting given by pathologists, deep-learning-aided mitosis counting can re-grade the patients whose risks were underestimated. The applications proved that the predictive models based on hybrid neural networks were able to analyse multiform medical data for generating data-based cancer outcome prediction. The results can be used for realising personalised treatment planning, evaluating treatment quality, and aiding clinical decision-making