The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"

Overview

DAGAN

This is the official implementation code for DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction published in IEEE Transactions on Medical Imaging (2018).
Guang Yang*, Simiao Yu*, et al.
(* equal contributions)

If you use this code for your research, please cite our paper.

@article{yang2018_dagan,
	author = {Yang, Guang and Yu, Simiao and Dong, Hao and Slabaugh, Gregory G. and Dragotti, Pier Luigi and Ye, Xujiong and Liu, Fangde and Arridge, Simon R. and Keegan, Jennifer and Guo, Yike and Firmin, David N.},
	journal = {IEEE Trans. Med. Imaging},
	number = 6,
	pages = {1310--1321},
	title = {{DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction}},
	volume = 37,
	year = 2018
}

If you have any questions about this code, please feel free to contact Simiao Yu ([email protected]).

Prerequisites

The original code is in python 3.5 under the following dependencies:

  1. tensorflow (v1.1.0)
  2. tensorlayer (v1.7.2)
  3. easydict (v1.6)
  4. nibabel (v2.1.0)
  5. scikit-image (v0.12.3)

Code tested in Ubuntu 16.04 with Nvidia GPU + CUDA CuDNN (whose version is compatible to tensorflow v1.1.0).

How to use

  1. Prepare data

    1. Data used in this work are publicly available from the MICCAI 2013 grand challenge (link). We refer users to register with the grand challenge organisers to be able to download the data.
    2. Download training and test data respectively into data/MICCAI13_SegChallenge/Training_100 and data/MICCAI13_SegChallenge/Testing_100 (We randomly included 100 T1-weighted MRI datasets for training and 50 datasets for testing)
    3. run 'python data_loader.py'
    4. after running the code, training/validation/testing data should be saved to 'data/MICCAI13_SegChallenge/' in pickle format.
  2. Download pretrained VGG16 model

    1. Download 'vgg16_weights.npz' from this link
    2. Save 'vgg16_weights.npz' into 'trained_model/VGG16'
  3. Train model

    1. run 'CUDA_VISIBLE_DEVICES=0 python train.py --model MODEL --mask MASK --maskperc MASKPERC' where you should specify MODEL, MASK, MASKPERC respectively:
    • MODEL: choose from 'unet' or 'unet_refine'
    • MASK: choose from 'gaussian1d', 'gaussian2d', 'poisson2d'
    • MASKPERC: choose from '10', '20', '30', '40', '50' (percentage of mask)
  4. Test trained model

    1. run 'CUDA_VISIBLE_DEVICES=0 python test.py --model MODEL --mask MASK --maskperc MASKPERC' where you should specify MODEL, MASK, MASKPERC respectively (as above).

Results

Please refer to the paper for the detailed results.

Owner
TensorLayer Community
A neutral open community to promote AI technology.
TensorLayer Community
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Subspace Adversarial Training Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However,

15 Sep 02, 2022
PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

Yulun Zhang 1.2k Dec 26, 2022
Pre-training of Graph Augmented Transformers for Medication Recommendation

G-Bert Pre-training of Graph Augmented Transformers for Medication Recommendation Intro G-Bert combined the power of Graph Neural Networks and BERT (B

101 Dec 27, 2022
BOVText: A Large-Scale, Multidimensional Multilingual Dataset for Video Text Spotting

BOVText: A Large-Scale, Bilingual Open World Dataset for Video Text Spotting Updated on December 10, 2021 (Release all dataset(2021 videos)) Updated o

weijiawu 47 Dec 26, 2022
git《Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction》(ECCV 2020) GitHub:

Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction Code for the ECCV 2020 paper by Yiming Qian and Yasutaka Furukawa Getting

37 Dec 04, 2022
Implementation of the master's thesis "Temporal copying and local hallucination for video inpainting".

Temporal copying and local hallucination for video inpainting This repository contains the implementation of my master's thesis "Temporal copying and

David Álvarez de la Torre 1 Dec 02, 2022
Spatial-Location-Constraint-Prototype-Loss-for-Open-Set-Recognition

Spatial Location Constraint Prototype Loss for Open Set Recognition Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for

Xia Ziheng 12 Jun 24, 2022
Solve a Rubiks Cube using Python Opencv and Kociemba module

Rubiks_Cube_Solver Solve a Rubiks Cube using Python Opencv and Kociemba module Main Steps Get the countours of the cube check whether there are tota

Adarsh Badagala 176 Jan 01, 2023
This repository contains PyTorch code for Robust Vision Transformers.

This repository contains PyTorch code for Robust Vision Transformers.

117 Dec 07, 2022
A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

3k Jan 02, 2023
Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

DD3D: "Is Pseudo-Lidar needed for Monocular 3D Object detection?" Install // Datasets // Experiments // Models // License // Reference Full video Offi

Toyota Research Institute - Machine Learning 364 Dec 27, 2022
A Kaggle competition: discriminate gender based on handwriting

Gender discrimination based on handwriting See http://fastml.com/gender-discrimination/ for description. prep_data.py - a first step chunk_by_authors.

Zygmunt Zając 22 Jul 20, 2022
COIN the currently largest dataset for comprehensive instruction video analysis.

COIN Dataset COIN is the currently largest dataset for comprehensive instruction video analysis. It contains 11,827 videos of 180 different tasks (i.e

86 Dec 28, 2022
Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
This repository contains the PyTorch implementation of the paper STaCK: Sentence Ordering with Temporal Commonsense Knowledge appearing at EMNLP 2021.

STaCK: Sentence Ordering with Temporal Commonsense Knowledge This repository contains the pytorch implementation of the paper STaCK: Sentence Ordering

Deep Cognition and Language Research (DeCLaRe) Lab 23 Dec 16, 2022
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Code for CCA-SSG model proposed in the NeurIPS 2021 paper From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.

Hengrui Zhang 44 Nov 27, 2022
Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling

Permutation Invariant Graph Generation via Score-Based Generative Modeling This repo contains the official implementation for the paper Permutation In

64 Dec 29, 2022
Resources complimenting the Machine Learning Course led in the Faculty of mathematics and informatics part of Sofia University.

Machine Learning and Data Mining, Summer 2021-2022 How to learn data science and machine learning? Programming. Learn Python. Basic Statistics. Take a

Simeon Hristov 8 Oct 04, 2022
The implementation of ICASSP 2020 paper "Pixel-level self-paced learning for super-resolution"

Pixel-level Self-Paced Learning for Super-Resolution This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resoluti

Elon Lin 41 Dec 15, 2022
NeWT: Natural World Tasks

NeWT: Natural World Tasks This repository contains resources for working with the NeWT dataset. ❗ At this time the binary tasks are not publicly avail

Visipedia 26 Oct 18, 2022