Segmentation of skin lesions and their attributes using Generative Adversarial Networks

01/30/2021 ∙ by Cristian Lazo, et al. ∙ 0 ∙ share

This work is about the semantic segmentation of skin lesion boundary and their attributes using Image-to-Image Translation with Conditional Adversarial Nets. Melanoma is a type of skin cancer that can be cured if detected in time. Segmentation into dermoscopic images is an essential procedure for computer-assisted diagnosis due to its existing artifacts typical of skin images. To alleviate the image annotation process, we propose to use a modified Pix2Pix network. The discriminator network learns the mapping from a dermal image as an input and a mask image of six channels as an output. Likewise, the discriminative network output called PatchGAN is varied for one channel and six output channels. The photos used come from the 2018 ISIC Challenge, where 500 photographs are used with their respective semantic map, divided into 75 training and 35 indices for all attributes of the segmentation map.

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Segmentation of skin lesions and their attributes using Generative Adversarial Networks

Authors

Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis

Skin Lesion Synthesis with Generative Adversarial Networks

Segmentation Guided Image-to-Image Translation with Adversarial Networks

MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network

Skin Lesion Segmentation: U-Nets versus Clustering

Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification

Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

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