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Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
Skin lesion segmentation is a vital task in skin cancer diagnosis and fu...
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Skin Lesion Synthesis with Generative Adversarial Networks
Skin cancer is by far the most common type of cancer. Early detection is...
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Segmentation Guided Image-to-Image Translation with Adversarial Networks
Recently image-to-image translation has received increasing attention, w...
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MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network
Skin lesion segmentation in dermoscopic images is a challenge due to the...
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Skin Lesion Segmentation: U-Nets versus Clustering
Many automatic skin lesion diagnosis systems use segmentation as a prepr...
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Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
Melanoma is a curable aggressive skin cancer if detected early. Typicall...
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Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
Mycosis fungoides (MF) is a rare, potentially life threatening skin dise...
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Segmentation of skin lesions and their attributes using Generative Adversarial Networks
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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