Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …

Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

Lesion detectors based on deep learning can assist doctors in diagnosing diseases.
However, the performance of current detectors is likely to be unsatisfactory due to the …

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 …

Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Unsupervised domain adaption (UDA), which aims to enhance the segmentation
performance of deep models on unlabeled data, has recently drawn much attention. In this …

Imaging plays a fundamental role in the effective diagnosis, staging, management, and
monitoring of various cardiac pathologies. Successful radiological analysis relies on …

Abstract Domain shift, a phenomenon when there exists distribution discrepancy between
training dataset (source domain) and test dataset (target domain), is very common in …

Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …

Featured Application Variational autoencoders, which are a type of neural network, are
introduced in this study as a means to virtually increase the sample size of clinical studies …