Progress in digital pathology is hindered by high-resolution images and the prohibitive cost
of exhaustive localized annotations. The commonly used paradigm to categorize pathology …

In digital pathology, whole-slide images (WSIs) are widely used for applications such as
cancer diagnosis and prognosis prediction. Vision transformer (ViT) models have recently …

Learning good representation of giga-pixel level whole slide pathology images (WSI) for
downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to …

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

Deep learning has achieved great success in processing large size medical images such as
histopathology slides. However, conventional deep learning methods cannot handle the …

A large-scale labeled dataset is a key factor for the success of supervised deep learning in
histopathological image analysis. However, exhaustive annotation requires a careful visual …

In recent years, computer vision techniques have made large advances in image recognition
and been applied to aid radiological diagnosis. Computational pathology aims to develop …

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 …

Histopathology image analysis plays an important role in the treatment and diagnosis of
cancer. However, analysis of whole slide images (WSI) with deep learning is challenging …

Convolutional neural networks can be trained to perform histology slide classification using
weak annotations with multiple instance learning (MIL). However, given the paucity of …