Medical Image Generation

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 )


Latest papers without code

Conditional Generation of Medical Images via Disentangled Adversarial Inference

8 Dec 2020

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.

Data Augmentation Image Generation +2

Image Translation for Medical Image Generation -- Ischemic Stroke Lesions

5 Oct 2020

Here, we demonstrate with the example of ischemic stroke that a significant improvement in lesion segmentation is feasible using deep learning-based data augmentation.

Data Augmentation Image-to-Image Translation +3

Medical Image Generation using Generative Adversarial Networks

19 May 2020

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.

Image Augmentation Image Reconstruction +3

SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis

6 Aug 2019

Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks.

Computed Tomography (CT) Data Augmentation +4

Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks

26 Jul 2018

Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models.

Data Augmentation Image Generation +2