A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor.

Automated brain lesion detection is an important and very challenging clinical diagnostic task, due to the lesions'different sizes, shapes, contrasts, and locations. Recently deep learning has shown promising progresses in many application fields, thereby motivating us to apply this technique for such an important problem. In this paper, we propose an automatic end-to-end trainable architecture for heterogeneous brain tumor segmentation through adversarial training for the BraTS-2017 challenge. Inspired by classical generative adversarial network, the proposed network has two components: the "Discriminator" and the "Generator". We use a patient-wise fully convolutional neural networks (FCNs) as the segmentor network to generate segmentation label maps. The discriminator network is patient-wise fully convolutional neural networks (FCNs) with L1 loss that discriminates segmentation maps coming from the ground truth or from the segmentor network. We propose an end-to-end trainable CNNs for survival day prediction based on deep learning techniques. The experimental results demonstrate the ability of the propose approaches for both tasks of BraTS-2017 challenge. Our patient-wise cGAN achieved competitive results in the BraTS-2017 challenges.