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 …

With applications in object detection, image feature extraction, image classification, and
image segmentation, artificial intelligence is facilitating high-throughput analysis of image …

With the rapid development of image scanning techniques and visualization software, whole
slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis …

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 …

Abstract Machine vision is being employed in defect detection, size measurement, pattern
recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection …

Image segmentation is consistently an important task for computer vision and the analysis of
medical images. The analysis and diagnosis of histopathology images by using efficient …

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 …

Recently, deep learning frameworks have been shown to be successful and efficient in
processing digital histology images for various detection and classification tasks. Among …

We propose a novel deep learning method called Boundary-Enhanced Segmentation
Network (BESNet) for the detection and semantic segmentation of cells on histopathological …

High-resolution microscopy images of tissue specimens provide detailed information about
the morphology of normal and diseased tissue. Image analysis of tissue morphology can …