Medical Image Generation
15 papers with code • 2 benchmarks • 2 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
BCI
Most implemented papers
NiftyNet: a deep-learning platform for medical imaging
NifTK/NiftyNet
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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
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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
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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
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Skin cancer is by far the most common type of cancer.
Towards Adversarial Retinal Image Synthesis
costapt/vess2ret
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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
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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.
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
liaohaofu/adn
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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.
Melanoma Detection using Adversarial Training and Deep Transfer Learning
hasibzunair/adversarial-lesions
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Journal of Physics in Medicine and Biology 2020
In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation.
Image Translation for Medical Image Generation -- Ischemic Stroke Lesions
We demonstrate with the example of ischemic stroke that an improvement in lesion segmentation is feasible using deep learning based augmentation.
