Medical Image Generation
29 papers with code • 5 benchmarks • 4 datasets
Medical image generation is the task of synthesising new medical images.
( Image credit: Towards Adversarial Retinal Image Synthesis )
Benchmarks
These leaderboards are used to track progress in Medical Image Generation
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ChestX-ray14
ACDC
BCI
Leishmania parasite dataset
Latest papers with no code
Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images
Novel 3D network architectures are proposed for both the generator and discriminator of the GAN model to significantly reduce the number of parameters while maintaining the quality of image generation.
GANs for Medical Image Synthesis: An Empirical Study
The top-performing GANs are capable of generating realistic-looking medical images by FID standards that can fool trained experts in a visual Turing test and comply to some metrics.
Conditional Generation of Medical Images via Disentangled Adversarial Inference
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.
Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images
Medical image datasets are usually imbalanced, due to the high costs of obtaining the data and time-consuming annotations.
Medical Image Generation using Generative Adversarial Networks
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.
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks.
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks
Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models.
