In this chapter, we present a deep learning-based, joint nuclei segmentation and fine-
grained classification method in histopathology images, which solves the nuclei …

Nuclei segmentation and classification are two important tasks in the histopathology image
analysis, because the morphological features of nuclei and spatial distributions of different …

The detection and classification of nuclei play an important role in the histopathological
analysis. It aims to find out the distribution of nuclei in the histopathology images for the next …

Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is
a challenging task due to a variety of issues, such as color inconsistency that results from the …

Segmentation and classification of cell nuclei in histopathology images using deep neural
networks (DNNs) can save pathologists' time for diagnosing various diseases, including …

In cancer, a variety of cell types, along with their local density and spatial organization within
tissues, play a key role in driving cancer progression and modulating patient outcomes. At …

Background Since nuclei segmentation in histopathology images can provide key
information for identifying the presence or stage of a disease, the images need to be …

This paper presents a novel computerized technique for the segmentation of nuclei in
hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to …

Nucleus segmentation and classification are the prerequisites in the workflow of digital
pathology processing. However, it is very challenging due to its high-level heterogeneity and …

Nuclei segmentation is a fundamental and important task in histopathological image
analysis. However, it still has some challenges such as difficulty in segmenting the …