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) aims to learn the mapping between a bag of instances and
the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN) …

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects
(instances), where the individual instance labels are ambiguous. In this setting, supervised …

We present a new approach to multiple instance learning (MIL) that is particularly effective
when the positive bags are sparse (ie contain few positive instances). Unlike other SVM …

Learning representations for individual instances when only bag-level labels are available is
a fundamental challenge in multiple instance learning (MIL). Recent works have shown …

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 …

Multi-instance learning (MIL) has been widely applied to diverse applications involving
complicated data objects, such as images and genes. However, most existing MIL …

Multiple instance learning (MIL) is a paradigm in supervised learning that deals with the
classification of collections of instances called bags. Each bag contains a number of …

Although attention mechanisms have been widely used in deep learning for many tasks,
they are rarely utilized to solve multiple instance learning (MIL) problems, where only a …

Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning
because it allows a bag of instances to share one label. Bag mapping transforms a bag into …