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

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

Colonic adenocarcinoma is a disease severely endangering human life caused by mucosal
epidermal carcinogenesis. The segmentation of potentially cancerous glands is the key in …

Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation
in histology images is challenging in variable conditions (clinical wild), such as poor staining …

Background In digital pathology, the morphology and architecture of prostate glands have
been routinely adopted by pathologists to evaluate the presence of cancer tissue. The …

The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology
images with a wide range of histologic grades of cancer is quite challenging. The …

Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …

Accurate gland segmentation is critical in determining adenocarcinoma. Automatic gland
segmentation methods currently suffer from challenges such as less accurate edge …

Simple Summary Differentiating growth patterns of the tumor glands in prostate biopsy tissue
images is a challenging task for pathologists. Therefore, advanced technology, especially …

The need for time and attention, given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …