Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

Colon cancer causes deaths of about half a million people every year. Common method of
its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is …

Gland segmentation is an important step to automate the analysis of biopsies that contain
glandular structures. However, this remains a challenging problem as the variation in …

This paper reports a new structural method to mathematically represent and quantify a tissue
for the purpose of automated and objective cancer diagnosis and grading. Unlike the …

Quantitative histomorphometry (QH) refers to the application of advanced computational
image analysis to reproducibly describe disease appearance on digitized histopathology …

Digital image analysis of histological datasets is a currently expanding field of research. With
different stains, magnifications and types of tissues, histological images are inherently …

The accurate prediction of cancer from microscopic biopsy images has always been a major
challenge for medical practitioners and pathologists who manually observe the shape and …

In recent years, there has been a great effort in the research of implementing automated
diagnostic systems for tissue images. One major challenge in this implementation is to …

The use of computational techniques in the processing of histopathological images allows
the study of the structural organization of tissues and their pathological changes. The overall …

Spatially Embedded Random Networks such as the Waxman random graph have been
used in many settings for synthesizing networks. Prior to our work, there existed no software …