Generative multi-adversarial network for striking the right balance in abdominal image segmentation.

ABSTRACT: Purpose The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images.Method The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handl

SUBMITTER: Rezaei M 

PROVIDER: S-EPMC7603459 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature