Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

The advent of whole slide imaging has brought advanced computer-aided diagnosis via
medical imaging and artificial intelligence technologies in digital pathology. The …

Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …

Histopathology images are used for the diagnosis of the cancerous disease by the
examination of tissue with the help of Whole Slide Imaging (WSI) scanner. A decision …

Breast cancer in women is the second most common cancer worldwide. Early detection of
breast cancer can reduce the risk of human life. Non-invasive techniques such as …

Abstract Machine learning techniques, especially deep learning techniques such as
convolutional neural networks, have been successfully applied to general image …

Breast cancer is a very common type of cancer in women around the world and more so in
India. It affects not only women but also men. In India, we have a very disturbing trend of …

Computational histopathology involves CAD for microscopic analysis of stained
histopathological slides to study presence, localization or grading of disease. An important …

Stain normalization of microscopic images is the first pre-processing step in any computer-
assisted automated diagnostic tool. This paper proposes Geometry-inspired Chemical …

Stain normalization is an important processing task for computer-aided diagnosis (CAD)
systems in modern digital pathology. This task reduces the color and intensity variations …