Abstract Representation learning from Gigapixel Whole Slide Images (WSI) poses a
significant challenge in computational pathology due to the complicated nature of tissue …

Whole slide image (WSI) classification is a critical task in computational pathology, requiring
the processing of gigapixel-sized images, which is challenging for current deep-learning …

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

This paper introduces MAD-MIL, a Multi-head Attention-based Deep Multiple Instance
Learning model, designed for weakly supervised Whole Slide Images (WSIs) classification …

Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for
Whole Slide Image (WSI) analysis is challenging given the complexity of gigapixel slides …

The classification of gigapixel histopathology images with deep multiple instance learning
models has become a critical task in digital pathology and precision medicine. In this work …

Multiple Instance Learning (MIL) has become the predominant approach for classification
tasks on gigapixel histopathology whole slide images (WSIs). Within the MIL framework …

Whole Slide Image (WSI) analysis is a powerful method to facilitate the diagnosis of cancer
in tissue samples. Automating this diagnosis poses various issues, most notably caused by …

Whole slide image (WSI) classification is a critical task in computational pathology. However,
the gigapixel-size of such images remains a major challenge for the current state of deep …

The rapid development of histopathology scanners allowed the digital transformation of
pathology. Current devices fastly and accurately digitize histology slides on many …