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 …

Background The approaches based on vision transformers (ViTs) are advancing the field of
medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have …

Background and objective Cancer grading in pathology image analysis is a major task due
to its importance in patient care, treatment, and management. The recent developments in …

Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-
aided pathological diagnosis, has rarely been studied due to the limitations in datasets and …

Deep Neural Networks (DNNs) are expected to provide explanation for users to understand
their black-box predictions. Saliency map is a common form of explanation illustrating the …

Deep learning in digital pathology brings intelligence and automation as substantial
enhancements to pathological analysis, the gold standard of clinical diagnosis. However …

Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires
high-level cognition and commonsense reasoning ability about the real world. In recent …

The increasing integration of deep learning with computational pathology has amplified the
use of whole slide images (WSIs) in modern clinical diagnosis. However, direct loading of an …

Background: Histological examination of tumor draining lymph nodes (LNs) plays a vital role
in cancer staging and prognostication. However, as soon as a LN is classed as metastasis …

Breast cancer is one of the most common type of cancer affecting women worldwide, and a
leading cause of cancer-related deaths. Histopathology image analysis plays a vital role in …