RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
This work addresses data scarcity in medical imaging for researchers and clinicians, but it is incremental as it builds on existing GAN methods.
The paper tackles the challenge of limited medical imaging data by proposing a deep convolutional conditional GAN to generate PET images for different lesion classes and normal cases, achieving promising results with a single model trained on small sample sizes.
One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in images. Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions. In this paper, we propose a deep convolutional conditional generative adversarial network to generate MIP positron emission tomography image (PET) which is a 2D image that represents a 3D volume for fast interpretation, according to different lesions or non lesion (normal). The advantage of our proposed method consists of one model that is capable of generating different classes of lesions trained on a small sample size for each class of lesion, and showing a very promising results. In addition, we show that a walk through a latent space can be used as a tool to evaluate the images generated.