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 …

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

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

Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Histopathology imaging is one of the key methods used to determine the presence of
cancerous cells. However, determining the results from such medical images is a tedious …

Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

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

Transformers perform well in natural language processing tasks and have made many
breakthroughs in computer vision. In medical image processing, transformers are …

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 …