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 …

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 …

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

Whole slide image (WSI) refers to a type of high-resolution scanned tissue image, which is
extensively employed in computer-assisted diagnosis (CAD). The extremely high resolution …

Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised
classification in whole slide image (WSI) based pathology diagnosis. However, the current …

Multiple instance learning (MIL) has emerged as a popular method for classifying
histopathology whole slide images (WSIs). However, existing approaches typically rely on …

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 …

In recent years, the availability of digitized Whole Slide Images (WSIs) has enabled the use
of deep learning-based computer vision techniques for automated disease diagnosis …

Whole slide histopathology images (WSIs) play a crucial role in diagnosing lymph node
metastasis of breast cancer, which usually lack fine-grade annotations of tumor regions and …

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 …