12 papers with code • 2 benchmarks • 1 datasets
Medical image generation is the task of synthesising new medical images.
( Image credit: Towards Adversarial Retinal Image Synthesis )
Current practices in using cGANs for medical image generation, only use a single variable for image generation (i. e., content) and therefore, do not provide much flexibility nor control over the generated image.
Here, we demonstrate with the example of ischemic stroke that a significant improvement in lesion segmentation is feasible using deep learning-based data augmentation.
Medical image datasets are usually imbalanced, due to the high costs of obtaining the data and time-consuming annotations.
Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community which has gained significant attention from the last few years in identifying the internal structure of multimodal medical imaging data.
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks.
Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models.