Segmenting histology images into diagnostically relevant regions is imperative to support
timely and reliable decisions by pathologists. To this end, computer-aided techniques have …

Numerous patch-based methods have recently been proposed for histological image based
breast cancer classification. However, their performance could be highly affected by ignoring …

Nuclei instance segmentation and classification in histology plays a major role in routine
pathology image examination, which enable morphological features analysis that further …

Medical image segmentation assists in computeraided diagnosis, surgeries, and treatment.
Digitize tissue slide images are used to analyze and segment glands, nuclei, and other …

Unprecedented breakthroughs in the development of graphical processing systems have
led to great potential for deep learning (DL) algorithms in analyzing visual anatomy from …

Abstract Background and Objective Whole slide image (WSI) classification and lesion
localization within giga-pixel slide are challenging tasks in computational pathology that …

Whole-slide image (WSI) classification is fundamental to computational pathology, which is
challenging in extra-high resolution, expensive manual annotation, data heterogeneity, etc …

One of the major obstacles in reaching diagnostic consensus is observer variability. With the
recent success of artificial intelligence, particularly the deep networks, the question emerges …

Cancer grading in pathology image analysis is one of the most critical tasks since it is
related to patient outcomes and treatment planning. Traditionally, it has been considered a …

Researchers working on computational analysis of Whole Slide Images (WSIs) in
histopathology have primarily resorted to patch-based modelling due to large resolution of …