Improving Skin Lesion Segmentation with Generative Adversarial Networks
Abstract: This paper proposes a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the image segmentation field, and a Convolutional-Deconvolutional Neural Network (CDNN) to automatically generate lesion segmentation mask from dermoscopic images. Training the CDNN with our GAN generated data effectively improves the state-of-the-art.
Citation:
Pollastri, Federico; Bolelli, Federico; Paredes, Roberto; Grana, Costantino "Improving Skin Lesion Segmentation with Generative Adversarial Networks" 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), vol. 2018-, Karlstad, Sweden, pp. 442 -443 , Jun 18-21, 2018 DOI: 10.1109/CBMS.2018.00086
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- DOI: 10.1109/CBMS.2018.00086