In recent years, computational pathology has emerged as a discipline representing big-data
based approaches for the diagnosis and prognosis of cancer patients using different …

Digital histology images are amenable to the application of convolutional neural networks
(CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally …

Deep learning advancements in computer vision have led to substantially improved
performances which for a few tasks even exceed the respective performance of human …

Computational pathology refers to applying deep learning techniques and algorithms to
analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led …

Cancer refers to a group of diseases characterized by an uncontrolled proliferation of cells
with underlying genetic mutations that can be arranged in solid masses forming tumors. The …

Histopathological image analysis has emerged as a pivotal tool in the field of colorectal
cancer diagnosis and prognosis. As the incidence of colorectal cancer continues to rise …

Motivation Digital pathology supports analysis of histopathological images using deep
learning methods at a large-scale. However, applications of deep learning in this area have …

Histopathology images; microscopy images of stained tissue biopsies contain fundamental
prognostic information that forms the foundation of pathological analysis and diagnostic …

Abstract The Convolutional Neural Network (CNN) is intended to generalize and
automatically learn spatial hierarchies of features, using stacked convolution-pooling layers …

Cancer histology reflects underlying molecular processes and disease progression and
contains rich phenotypic information that is predictive of patient outcomes. In this study, we …