Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …

The investigation of uncertainty is of major importance in risk-critical applications, such as
medical image segmentation. Belief function theory, a formal framework for uncertainty …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Deep learning has shown promising contributions in medical image segmentation with
powerful learning and feature representation abilities. However, it has limitations for …

Modern healthcare applications require fast detection of human diseases which saves the
human life on time. In order to achieve this, the detection or identification of such diseases …

As the primary contributions of this thesis, the current state-of-the-art uncertainty
quantification techniques for medical classification problems are reviewed comprehensively …