Code repository for Frontiers article 'Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT'

Official implementations of the pixel-wise and cycle-consistency GAN models for multi-contrast MRI synthesis

It describes MRI to CT conversion, 3T to 7T conversion using Generative Adversarial Network (GAN).

Generating randomized brain MRI images from random noise using a GAN. Additionally translating from one image domain to another with a conditional GAN (pix2pix): Segmenting brain anatomy - Generating brain MRI from the segmentation - Augmenting the translation of image modalities in a limited dataset to perform ischemic stroke segmentation.

Structure preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks (CVPRW 2020)

Deep Learning Project Utilizing GAN Architectures for Super-Resolution of MRI Images

Application of CollaGAN (Collaborative GAN) for MRI Image Imputation

DeepFake detection using GAN and DeepLearning

Official repository for the paper "Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs" by Furkan Ozcelik, Bhavin Choksi, Milad Mozafari, Leila Reddy, Rufin VanRullen.

Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images

Repo of the paper "Generative Adversarial Networks as an advanced data augmentation technique for MRI data" by Filippos Konidaris, Thanos Tagaris, Maria Sdraka and Andreas Stafylopatis

Generates CT images from MRI using cycleGAN.

CycleGAN, a variation of GAN (Generative Adversarial Network) which works well with unpaired data thus fits best for medical images. Used CycleGAN for T1-weighted to T2-weighted in MRI image translation.

In medical domain cross modality image synthesis suffers from multiple issues such as context-misalignment, image distortion, image blurriness, and loss of details. The fundamental objective behind this study is to address these issues in estimating synthetic Computed tomography (sCT) scans from T2-weighted Magnetic Resonance Imaging (MRI) scans in order to achieve MRI-guided Radiation Treatment (RT).

An improved conditional wasserstein generative adversarial network (ICW-GAN) that is trained to generate synthetic fMRI data samples.

Image-to-Image Translation in PyTorch. Cycle-consistency GAN models for multi-contrast MRI synthesis

This project is about investigating the capabilities of GAN to translate spinal CT to MRI scans and vice versa.

A capstone project dedicated to leveraging Style GAN (Generative Adversarial Network) to generate Brain MRI images of different contrasts

The study works on generating CT images from MRI images, where unsupervised learning was used using VAE-CycleGan. Since the number of samples included in the data set used in the study, and therefore in this case we are in a state of epistemic uncertainty, therefore probabilistic models were used in forming the latent space.

Coupled GAN for fusion of MRI and PET image-features for AD diagnosis.

This repository is implementation of MRI-only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach

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Build a Generative adversarial model (modified U-Net) which can generate artificial MRI images of different contrast levels from existing MRI scans.

A U-Net based end-to-end MRI motion correction. With/Without GAN version is available.

This repository uses Cycle GAN for unpaired image to image translation.

Official Repository for the paper "Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs".

generate 3D brain MRI using Progressive Growing GAN

This repository explores and implements techniques for generating medical images using GANs, Diffusion models. In this project, we will develop models capable of producing realistic medical images, including MRI, CT, and X-ray scans. These models can aid in medical research, education, and improving the quality of training datasets.

Generation missing MRI using GANs - master thesis from Politechnic of Milan