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

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 …

Histopathological images provide the medical evidences to help the disease diagnosis.
However, pathologists are not always available or are overloaded by work. Moreover, the …

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

This paper addresses complex challenges in histopathological image analysis through three
key contributions. Firstly it introduces a fast patch selection method FPS for whole-slide …

Transformer has been widely used in histopathology whole slide image (WSI) classification
for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wise …

In this paper, we present a histopathology image classification method with supervised intra-
embedding of Fisher vectors. Recently in general computer vision, Fisher encoding …

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 …

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 …

Pathology image classification plays an important role in cancer diagnosis and precision
treatment. Convolutional neural network has been widely employed in pathology image …