Medical Image Generation

12 papers with code • 2 benchmarks • 1 datasets

Medical image generation is the task of synthesising new medical images.

( Image credit: Towards Adversarial Retinal Image Synthesis )


Latest papers with code

Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation

AugustDS/synthetic-medical-benchmark 29 Nov 2020

The synthetic images ideally have, in aggregate, similar statistical properties to those of a source dataset but do not contain sensitive personal information.

Computed Tomography (CT) Image Generation +2

29 Nov 2020

MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education

cyrilzakka/stylegan2-tpu 11 Oct 2020

During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions.

Radiologist Binary Classification

11 Oct 2020

Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs

BradSegal/CXR_PGGAN 7 Oct 2020

We apply a PGGAN to the task of unsupervised x-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples.

Conditional Image Generation Data Augmentation +2

07 Oct 2020

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.

Conditional Image Generation General Classification +6

14 Apr 2020

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.

Computed Tomography (CT) Image-to-Image Translation +2

03 Aug 2019

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

carrenD/Medical-Cross-Modality-Domain-Adaptation 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.

Cardiac Segmentation Domain Adaptation +1

19 Dec 2018

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.

Data Augmentation Domain Adaptation +3

19 Sep 2018

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.

Computed Tomography (CT) Image-to-Image Translation +1

20 Jun 2018

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.

Data Augmentation Image Generation +2

11 Sep 2017