Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

An efficient learner is one who reuses what they already know to tackle a new problem. For
a machine learner, this means understanding the similarities amongst datasets. In order to …

In many classification problems labels are relatively scarce. One context in which this occurs
is where we have labels for groups of instances but not for the instances themselves, as in …

This book is based on my Ph. D. dissertation completed at Ghent University (Belgium) and
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …

While a typical supervised learning framework assumes that the inputs and the outputs are
measured at the same levels of granularity, many applications, including global mapping of …

This article proposes a framework for model-based point pattern learning using point
process theory. Likelihood functions for point pattern data derived from point process theory …

Quantitative analysis of morphological changes in a cell nucleus is important for the
understanding of nuclear architecture and its relationship with pathological conditions such …

Open-source indicators such as social media can be very effective precursors for forecasting
future societal events. As events are often preceded by social indicators generated by …