Variable Augmented Network for Invertible Modality Synthesis-Fusion

The Code is created based on the method described in the following paper:
Variable Augmented Network for Invertible Modality Synthesis and Fusion
Y. Wang, R. Liu, Z. Li, S. Wang, C. Yang, Q. Liu
IEEE Journal of Biomedical and Health Informatics
Page: 2898 - 2909, Volume: 27 Issue: 6, 2023.
https://ieeexplore.ieee.org/abstract/document/10070774
Date : Sep. 1, 2021
Version : 1.0
The code and the algorithm are for non-comercial use only.
Copyright 2021, Department of Electronic Information Engineering, Nanchang University.

Optional parameters:

weight: Weight for forward loss.
epoch: Specifies number of iterations.

Visual illustration of the invertible medical image synthesis and fusion in variable augmentation manner

The training pipeline of iVAN

Two visualization results of synthesizing from T1 to T2

Three fusion results of T2-weighted MR and CT images

Train

Prepare your own datasets for VAN

You need to create at least two modality medical images from domain A /data/A and from domain B /data/B. Then you can train the model with the dataset flag --root1 './data/A' --root2 './data/B'. Optionally, you can create hold-out test datasets at ./data/A_test and ./data/B_test to test your model.

1to1

python train.py --task=1to1 --out_path="./exps/"

many to 1

python train.py --task=2to1 --out_path="./exps/"

resume training:

To fine-tune a pre-trained model, or resume the previous training, use the --resume flag

Test

python test.py --task=2to1 --out_path="./exps/" --ckpt="./exps/2to1/checkpoint/latest.pth"

python test.py --task=1to1 --out_path="./exps/" --ckpt="./exps/1to1/checkpoint/latest.pth"

Acknowledgement

The code is based on yzxing87/Invertible-ISP

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