Generative Adversarial Networks are a class of artificial intelligence algorithms that consist
of a generator and a discriminator trained simultaneously through adversarial training. GANs …

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 …

Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …

Abstract Machine-assisted pathological recognition has been focused on supervised
learning (SL) that suffers from a significant annotation bottleneck. We propose a semi …

Image analysis in the field of digital pathology has recently gained increased popularity. The
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …

Digital pathology technologies, including whole slide imaging (WSI), have significantly
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …

Digital pathology is gaining prominence among the researchers with developments in
advanced imaging modalities and new technologies. Generative adversarial networks …

Until recently, conventional biochemical staining had the undisputed status as well-
established benchmark for most biomedical problems related to clinical diagnostics …

The explosive growth of artificial intelligence (AI) technologies, especially deep learning
methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare …

Although generative adversarial network (GAN) based style transfer is state of the art in
histopathology color-stain normalization, they do not explicitly integrate structural …