Skin Lesion Synthesis with Generative Adversarial Networks
arXiv:1902.03253 · doi:10.1007/978-3-030-01201-4_32
Abstract
Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly. Currently, Deep Neural Networks are the state-of-the-art results on automated skin cancer classification. To push the results further, we need to address the lack of annotated data, which is expensive and require much effort from specialists. To bypass this problem, we propose using Generative Adversarial Networks for generating realistic synthetic skin lesion images. To the best of our knowledge, our results are the first to show visually-appealing synthetic images that comprise clinically-meaningful information.
Conference: ISIC Skin Image Analysis Workshop and Challenge @ MICCAI 2018
References in corpus (5)
- Improved Techniques for Training GANs
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- RECOD Titans at ISIC Challenge 2017
- Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)
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Cited by in corpus (11)
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- Knowledge Distillation approach towards Melanoma Detection
- Generation of microbial colonies dataset with deep learning style transfer
- Conditional Generation of Medical Images via Disentangled Adversarial Inference
- Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018
- The Six Fronts of the Generative Adversarial Networks
- Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
- (De)Constructing Bias on Skin Lesion Datasets
- Skin disease diagnosis with deep learning: a review
- eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases