SegAN: Adversarial Network with Multi-scale L 1 Loss for Medical Image Segmentation

Neuroinformatics, 2018.

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Abstract:

Inspired by classic Generative Adversarial Networks (GANs), 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 s...More

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