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Medical Image Generation

10 papers with code · Medical

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

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

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

11 Sep 2017NifTK/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

3 Aug 2019JunMa11/MICCAI2019-OpenSourcePapers

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

19 Dec 2018carrenD/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

20 Jun 2018simontomaskarlsson/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.

COMPUTED TOMOGRAPHY (CT) IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION

Towards Adversarial Retinal Image Synthesis

31 Jan 2017costapt/vess2ret

These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image.

IMAGE-TO-IMAGE TRANSLATION 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