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 …

A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Multiple instance learning (MIL) is a variation of supervised learning where a single class
label is assigned to a bag of instances. In this paper, we state the MIL problem as learning …

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 …

Abstract In conventional Magnetic Resonance (MR) image based methods, two stages are
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …

Of late, neural networks and Multiple Instance Learning (MIL) are both attractive topics in the
research areas related to Artificial Intelligence. Deep neural networks have achieved great …

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 …

We propose a framework for analyzing and comparing distributions, which we use to
construct statistical tests to determine if two samples are drawn from different distributions …

Abstract Multiple Instance Learning (MIL) has become an important topic in the pattern
recognition community, and many solutions to this problem have been proposed until now …

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 …