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

The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that
plays an important role in digital health applications. Medical images account for 90% of the …

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …

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 …

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide
images (WSIs) often process a large number of tiles (sub-images) and require aggregating …

In computation pathology, the pyramid structure of gigapixel Whole Slide Images (WSIs) has
recently been studied for capturing various information from individual cell interactions to …

Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular
pathways, genetic subtypes, and mutations associated with CRC is crucial for precision …

Image analysis can play an important role in supporting histopathological diagnoses of lung
cancer, with deep learning methods already achieving remarkable results. However, due to …

This comprehensive review aims to clarify the growing impact of Transformer‐based models
in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution …

In the domain of histopathology analysis, existing representation learning methods for
biomarkers prediction from whole slide images (WSIs) face challenges due to the complexity …