Medical Image Generation
Medical image generation is the task of synthesising new medical images.
State-of-the-art leaderboards
Greatest papers with code
NiftyNet: a deep-learning platform for medical imaging
• NifTK/NiftyNet
• 
NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
DATA AUGMENTATION IMAGE GENERATION MEDICAL IMAGE GENERATION REPRESENTATION LEARNING
Generative Adversarial Network in Medical Imaging: A Review
• xinario/awesome-gan-for-medical-imaging
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.
DATA AUGMENTATION DOMAIN ADAPTATION IMAGE RECONSTRUCTION IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
Here, we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image translation of T1- and T2-weighted MR images, by comparing generated synthetic MR images to ground truth images.
Towards Adversarial Retinal Image Synthesis
• costapt/vess2ret
•
These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image.
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
• carrenD/Med-CMDA
•
In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.
CARDIAC SEGMENTATION DOMAIN ADAPTATION MEDICAL IMAGE GENERATION
Skin Lesion Synthesis with Generative Adversarial Networks
• alceubissoto/gan-skin-lesion
•
Skin cancer is by far the most common type of cancer.
Synthetic Medical Images from Dual Generative Adversarial Networks
• HarshaVardhanVanama/Synthetic-Medical-Images
•
Currently there is strong interest in data-driven approaches to medical image classification.
IMAGE CLASSIFICATION IMAGE GENERATION MEDICAL IMAGE GENERATION