Deep learning has recently advanced in its applicability to computer vision challenges, and
medical imaging has become the most used technique in histopathology image analysis …

Computer aided diagnosis based on computational pathology combines the concepts of
pathology with computer science to develop automated mechanisms for interpretation of …

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …

Digital pathology is one of the most significant developments in modern medicine.
Pathological examinations are the gold standard of medical protocols and play a …

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 …

Nuclei segmentation and classification are two basic and essential tasks in computer-aided
diagnosis of digital pathology images, and those deep-learning-based methods have …

Nucleus segmentation represents the initial step for histopathological image analysis
pipelines, and it remains a challenge in many quantitative analysis methods in terms of …

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 …

Accurate segmentation of nuclei plays a critical role in pathology since assessments and
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …

In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell
nuclei analysis represent an important measure for cancer diagnosis. The most valuable …