Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …

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

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 …

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …

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 …

Multiple instance learning (MIL) is a typical weakly-supervised learning method where the
label is associated with a bag of instances instead of a single instance. Despite extensive …

Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …