2 papers
cs.CV2018
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation
Christopher Bowles, Roger Gunn, Alexander Hammers +1
Medical imaging is a domain which suffers from a paucity of manually annotated data for the training of learning algorithms. Manually delineating pathological regions at a pixel le…
cs.CV2018
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
Christopher Bowles, Liang Chen, Ricardo Guerrero +7
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not onl…