Generative adversarial networks (GANs) and their extensions have carved open many
exciting ways to tackle well known and challenging medical image analysis problems such …

Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …

Convolutional neural networks have been successfully applied to semantic segmentation
problems. However, there are many problems that are inherently not pixel-wise classification …

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche
terms that are increasingly appearing in scientific presentations as well as in the general …

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

Phenotypic image analysis is the task of recognizing variations in cell properties using
microscopic image data. These variations, produced through a complex web of interactions …

We present a novel method for cell segmentation in microscopy images which is inspired by
the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair …

Deep-learning workflows of microscopic image analysis are sufficient for handling the
contextual variations because they employ biological samples and have numerous tasks …

Background and objectives Spheroids are the most widely used 3D models for studying the
effects of different micro-environmental characteristics on tumour behaviour, and for testing …

Spheroids are the most widely used 3D models for studying the effects of different micro-
environmental characteristics on tumor behaviour, and for testing different preclinical and …