SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

6 Jun 2017Yuan XueTao XuHan ZhangRodney LongXiaolei Huang

Inspired by classic generative adversarial networks (GAN), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Brain Tumor Segmentation BRATS-2013 leaderboard SegAN Dice Score 0.84 # 1