Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis

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Deep LearningPTNet
Overview

Pyramid Transformer Net (PTNet)

Project | Paper

Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis.

PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer
Xuzhe Zhang1, Xinzi He1, Jia Guo2, Nabil Ettehadi1, Natalie Aw2, David Semanek2, Jonathan Posner2, Andrew Laine1, Yun Wang2
1Columbia University Department of Biomedical Engineering, 2CUMC Department of Psychiatry

Usage and Demo

Coming Soon

Prerequisites

  • Linux
  • Python3.6
  • NVIDIA GPU (11G memory or larger) + CUDA cuDNN

Getting Started

Installation

coming soon

Testing

coming soon

Dataset

coming soon

Training

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More Training/Test Details

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Citation

If you find this useful for your research, please use the following.

@article{zhang2021ptnet,
  title={PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer},
  author={Zhang, Xuzhe and He, Xinzi and Guo, Jia and Ettehadi, Nabil and Aw, Natalie and Semanek, David and Posner, Jonathan and Laine, Andrew and Wang, Yun},
  journal={arXiv preprint arXiv:2105.13993},
  year={2021}
}

Acknowledgments

This code borrows heavily from: Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, pix2pixHD, pytorch-CycleGAN-and-pix2pix.

Owner
Xuzhe Johnny Zhang
image processing; CV; deep learning; computer-assisted medical image diagnosis/interpretation
Xuzhe Johnny Zhang
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