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 …

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 …

Abstract Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis
analysis are promising directions in computational pathology. However, limited by …

Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment
of diseases; but, curation of accurate labels is time-consuming and limits the application of …

Existing Multi-Instance learning (MIL) methods for medical image classification typically
segment an image (bag) into small patches (instances) and learn a classifier to predict the …

Abstract Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label
is assigned to the whole bag of instances. An important class of MIL models is instance …

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 …

Digital pathology plays a pivotal role in the diagnosis and interpretation of diseases and has
drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and …

The classification of gigapixel-sized whole slide images (WSIs) with slide-level labels can be
formulated as a multiple-instance-learning (MIL) problem. State-of-the-art models often …

In multiple instance learning (MIL), how the instances determine the bag-labels is an
essential issue, both algorithmically and intrinsically. In this paper, we show that the …