We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …

The whole slide image (WSI) classification is often formulated as a multiple instance
learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI …

Bag-based multiple instance learning (MIL) methods have become the mainstream for
Whole Slide Image (WSI) classification. However, there are still three important issues that …

Whole slide image (WSI) classification often relies on deep weakly supervised multiple
instance learning (MIL) methods to handle gigapixel resolution images and slide-level …

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 …

Multi-instance learning (MIL) is widely adop-ted for automatic whole slide image (WSI)
analysis and it usually consists of two stages, ie, instance feature extraction and feature …

Abstract Multiple Instance Learning (MIL) is widely used in analyzing histopathological
Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data …

Multiple Instance Learning (MIL) and transformers are increasingly popular in
histopathology Whole Slide Image (WSI) classification. However, unlike human pathologists …

Weakly supervised whole slide image classification is usually formulated as a multiple
instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut …

Multiple instance learning (MIL) has been increasingly used in the classification of
histopathology whole slide images (WSIs). However, MIL approaches for this specific …