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Medical Image Generation

8 papers with code · Medical

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

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Greatest papers with code

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. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.

IMAGE GENERATION MEDICAL IMAGE GENERATION REPRESENTATION LEARNING

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 general computer vision community due to their capability of data generation without explicitly modelling the probability density function and robustness to overfitting. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into the training and imposing higher order consistency that is proven to be useful in many cases, such as in domain adaptation, data augmentation, and image-to-image translation.

DOMAIN ADAPTATION IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION

Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks

26 Jul 2018khcs/brain-synthesis-lesion-segmentation

Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI.

IMAGE GENERATION MEDICAL IMAGE GENERATION

Towards Adversarial Retinal Image Synthesis

31 Jan 2017costapt/vess2ret

Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. 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

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. It is shown that the implemented GAN models can synthesize visually realistic MR images (incorrectly labeled as real by a human).

IMAGE-TO-IMAGE TRANSLATION MEDICAL IMAGE GENERATION

Skin Lesion Synthesis with Generative Adversarial Networks

8 Feb 2019alceubissoto/gan-skin-lesion

Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly.

MEDICAL IMAGE GENERATION SKIN CANCER CLASSIFICATION

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

19 Dec 2018carrenD/Medical-Cross-Modality-Domain-Adaptation

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. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation.

CARDIAC SEGMENTATION DOMAIN ADAPTATION MEDICAL IMAGE GENERATION

Synthetic Medical Images from Dual Generative Adversarial Networks

6 Sep 2017HarshaVardhanVanama/Synthetic-Medical-Images

Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns regarding patient privacy.

IMAGE CLASSIFICATION IMAGE GENERATION MEDICAL IMAGE GENERATION