Abstract Multiple Instance Learning (MIL) is a learning paradigm, where training instances
are arranged in sets, called bags, and only bag-level labels are available during training …

Histopathology image classification is an important clinical task, and current deep learning-
based whole-slide image (WSI) classification methods typically cut WSIs into small patches …

Abstract Multiple Instance Learning (MIL) is a sub-domain of classification problems with
positive and negative labels and a" bag" of inputs, where the label is positive if and only if a …

Multi-instance learning (MIL) is a supervised learning where each example is a labeled bag
with many instances. The typical MIL strategies are to train an instance-level feature …

Multiple instance learning (MIL) trains models from bags of instances, where each bag
contains multiple instances, and only bag-level labels are available for supervision. The …

Abstract Weakly Supervised Video Anomaly Detection (WSVAD) plays a crucial role in
visual surveillance by effectively distinguishing anomalies from normality with only video …

Set-level problems become critical when we are interested in animals in pictures, links in
web pages, and components in drugs. The key issue is to measure the similarity between …

In multi-instance nonparallel plane learning (NPL), the training set is comprised of bags of
instances and the nonparallel planes are trained to classify the bags. Most of the existing …

Image has become an essential medium for expressing meaning and disseminating
information. Many images are uploaded to the Internet, among which some are …

In multiple instance learning (MIL), a bag represents a sample that has a set of instances,
each of which is described by a vector of explanatory variables, but the entire bag only has …