Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …

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

Histopathology is diagnosis based on visual examination of tissue sections under a
microscope. With the growing number of digitally scanned tissue slide images, computer …

In many computerized methods for cell detection, segmentation, and classification in digital
histopathology that have recently emerged, the task of cell segmentation remains a chief …

Histopathological images (HIs) are the gold standard for evaluating some types of tumors for
cancer diagnosis. The analysis of such images is time and resource-consuming and very …

Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …

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 …

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 …

The development of digital pathology and progression of state‐of‐the‐art algorithms for
computer vision have led to increasing interest in the use of artificial intelligence (AI) …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …