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 )

Most implemented papers

NiftyNet: a deep-learning platform for medical imaging

NifTK/NiftyNet 11 Sep 2017

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 20 Jun 2018

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 19 Dec 2018

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 8 Feb 2019

Skin cancer is by far the most common type of cancer.

Towards Adversarial Retinal Image Synthesis

costapt/vess2ret 31 Jan 2017

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 6 Sep 2017

Currently there is strong interest in data-driven approaches to medical image classification.

Generative Adversarial Network in Medical Imaging: A Review

xinario/awesome-gan-for-medical-imaging 19 Sep 2018

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 3 Aug 2019

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 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

MoPl90/image_translation 5 Oct 2020

We demonstrate with the example of ischemic stroke that an improvement in lesion segmentation is feasible using deep learning based augmentation.