Medical Image Generation
Medical image generation is the task of synthesising new medical images.
( Image credit: Towards Adversarial Retinal Image Synthesis )
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Greatest papers with code
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.
DATA AUGMENTATION DOMAIN ADAPTATION IMAGE RECONSTRUCTION IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION
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
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
• JunMa11/MICCAI2019-OpenSourcePapers
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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.
COMPUTED TOMOGRAPHY (CT) IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION METAL ARTIFACT REDUCTION
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
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.
COMPUTED TOMOGRAPHY (CT) IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION
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.
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.
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.
IMAGE CLASSIFICATION IMAGE GENERATION MEDICAL IMAGE GENERATION
Melanoma Detection using Adversarial Training and Deep Transfer Learning
Journal of Physics in Medicine and Biology 2020
• hasibzunair/adversarial-lesions
•
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.
CONDITIONAL IMAGE GENERATION IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION SKIN CANCER CLASSIFICATION SKIN LESION CLASSIFICATION TRANSFER LEARNING
A CNN-Based Blind Denoising Method for Endoscopic Images
Conference 2019
• ShaofengZou/A-CNN-Based-Blind-Denoising-Method
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The number of iterations is reduced about 36% by using transfer learning in our DIP process.
BLIND IMAGE QUALITY ASSESSMENT IMAGE DENOISING IMAGE ENHANCEMENT MEDICAL IMAGE GENERATION TRANSFER LEARNING