Interpreting biomedical data via deep neural networks

Machine learning technology has taken quantum leaps in the past few years. From the rise of voice recognition as an interface to interact with our computers, to self-organising photo albums and self-driving cars. Neural networks and deep learning contributed significantly to drive this revolution. Yet, biomedicine is one of the research areas that has yet to fully embrace the possibilities of deep learning. Engaged in a cross-disciplinary subject, researchers, and clinical experts are focused on machine learning and statistical signal processing techniques. The ability to learn hierarchical features makes deep learning models highly applicable to biomedicine and researchers have started to notice this. The first works of deep learning in biomedicine are emerging with applications in diagnostics and genomics analysis. These models offer excellent accuracy, even comparable to that of human doctors. Despite the exceptional classification performance of these models, they are still used to provide \textit{quantitative} results. Diagnosing cancer proficiently and faster than a human doctor is beneficial, but automatically finding which biomarkers indicate the existence of cancerous cells would be invaluable. This type of \textit{qualitative} insight can be enabled by the hierarchical features and learning coefficients that manifest in deep models. It is this \textit{qualitative} approach that enables the interpretability of data and explainability of neural networks for biomedicine, which is the overarching aim of this thesis. As such, the aim of this thesis is to investigate the use of neural networks and deep learning models for the qualitative assessment of biomedical datasets. The first contribution is the proposition of a non-iterative, data agnostic feature selection algorithm to retain original features and provide qualitative analysis on their importance. This algorithm is employed in numerous areas including Pima Indian diabetes and children tumour detection. Next, the thesis focuses on the topic of epilepsy studied through scalp and intracranial electroencephalogram recordings of human brain activity. The second contribution promotes the use of deep learning models for the automatic generation of clinically meaningful features, as opposed to traditional handcrafted features. Convolutional neural networks are adapted to accommodate the intricacies of electroencephalogram data and trained to detect epileptiform discharges. The learning coefficients of these models are examined and found to contain clinically significant features. When combined, in a hierarchical way, these features reveal useful insights for the evaluation of treatment effectivity. The final contribution addresses the difficulty in acquiring intracranial data due to the invasive nature of the recording procedure. A non-linear brain mapping algorithm is proposed to link the electrical activities recorded on the scalp to those inside the cranium. This process improves the generalisation of models and alleviates the need for surgical procedures. %This is accomplished via an asymmetric autoencoder that accounts for differences in the dimensionality of the electroencephalogram data and improves the quality of the data.