/VQ_AD_Diganosis

Vector Quantized Multi-modal Guidance for Alzheimer’s Disease Diagnosis Based on Feature Imputation

Primary LanguagePython

Vector Quantized Multi-modal Guidance for Alzheimer’s Disease Diagnosis Based on Feature Imputation

This repo contains PyTorch implementation of the paper: Vector Quantized Multi-modal Guidance for Alzheimer’s Disease Diagnosis Based on Feature Imputation

Paper Link

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Setup Instructions

  1. Convert your ADNI dataset into a h5py file. Example codes -> "datasets/ADNI2HDF5.py". Note that tabular data is not used in this project, you can ignore the tabular data part in this code.
  2. Install dependencies pip install -r ./requirements.txt

Run the Model

python train_kfold.py

Detail options of the training process is in "options.py"

References

If you think our research work helpful, please consider citing our original paper.

@inproceedings{zhang2023vector,
  title={Vector Quantized Multi-modal Guidance for Alzheimer’s Disease Diagnosis Based on Feature Imputation},
  author={Zhang, Yuanwang and Sun, Kaicong and Liu, Yuxiao and Ou, Zaixin and Shen, Dinggang},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={403--412},
  year={2023},
  organization={Springer}
}