Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …

Transformers have been widely used in many computer vision challenges and have shown
the capability of producing better results than convolutional neural networks (CNNs). Taking …

Histopathological image classification is a fundamental task in pathological diagnosis
workflow. It remains a huge challenge due to the complexity of histopathological images …

While machine learning is currently transforming the field of histopathology, the domain
lacks a comprehensive evaluation of state-of-the-art models based on essential but …

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 …

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 …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

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 …

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

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …