DermGAN: Synthetic Generation of Clinical Skin Images with Pathology

20 Nov 2019 Amirata Ghorbani Vivek Natarajan David Coz Yu-An Liu

Despite the recent success in applying supervised deep learning to medical imaging tasks, the problem of obtaining large and diverse expert-annotated datasets required for the development of high performant models remains particularly challenging. In this work, we explore the possibility of using Generative Adverserial Networks (GAN) to synthesize clinical images with skin condition... (read more)

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Methods used in the Paper


METHOD TYPE
Concatenated Skip Connection
Skip Connections
PatchGAN
Discriminators
ReLU
Activation Functions
Batch Normalization
Normalization
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions
Dropout
Regularization
Pix2Pix
Generative Models
Convolution
Convolutions
GAN
Generative Models