Medical Image Generation
30 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
ChestX-ray14
ACDC
BCI
Leishmania parasite dataset
Most implemented papers
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.
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
simontomaskarlsson/GAN-MRI
•
•
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.
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.
Skin Lesion Synthesis with Generative Adversarial Networks
alceubissoto/gan-skin-lesion
•
•
Skin cancer is by far the most common type of cancer.
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
liaohaofu/adn
•
•
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training.
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization
We employed the STARE dataset for external validation, ensuring a comprehensive assessment of the proposed approach.
Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend
mckellwoodland/fid-med-eval
•
•
A recent trend is to adapt FID to medical imaging through feature extractors trained on medical 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.
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.
Generative Adversarial Network in Medical Imaging: A Review
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.
