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On the design of convolutional neural networks for automatic detection of Alzheimer’s disease

Update

We published a new paper on deep learning for dementia at Nature Scientific Reports. In the new paper, we compare the deep learning model with a volume/thickness model. The volume and thickness data are extracted using the Freesurfer with quality control done by radiologist.

If you would like to access the volume and thickness data, please download it from our new repo.

Introduction

In this project, we focus on how to design CNN for Alzheimer's detection. we provide evidence that

  • instance normalization outperforms batch normalization
  • early spatial downsampling negatively affects performance
  • widening the model brings consistent gains while increasing the depth does not
  • incorporating age information yields moderate improvement.

Together, these insights yield an increment of approximately 14% in test accuracy over existing models.

This repository contains the code for the paper On the design of convolutional neural networks for automatic detection of Alzheimer’s disease. Author: Sheng Liu, Chhavi Yadav, Carlos Fernandez-Granda, Narges Razavian.

Prerequisites

  • Python 3.6
  • PyTorch 0.4
  • torchvision
  • progress
  • matplotlib
  • numpy
  • visdom

License

This repository is licensed under the terms of the GNU AGPLv3 license.

Download ADNI data

  1. Request approval and register at ADNI website
  2. Download both the scans and the clinical data. From the main page click on PROJECTS and ADNI. To download the imaging data, click on Download and choose Image collections. In the Advanced search tab, untick ADNI 3 and tick MRI to download all the MR images.
  3. In the Advanced search results tab, click Select All and Add To Collection. Finally, in the Data Collection tab, select the collection you just created, tick All and click on Advanced download. We advise you to group files as 10 zip files. To download the clinical data, click on Download and choose Study Data. Select all the csv files which are present in ALL by ticking Select ALL tabular data and click Download.

Data Preprocessing

Data Preprocessing with Clinica:

  1. Convert data into BIDS format: please read the docs on Clinica website, and install required softwares and download the required clinical files. Note that we first preprocess the training set to generate the template and use the template to preprocess validation and test set. You can find the link to download the template we used for data preprocessing. You can find the script we use to run the converter at /datasets/files:
run_convert.sh
  1. preprocess converted and splitted data: you can refer our scripts at /datasets/files. For training data, refer:
run_adni_preprocess.sh

For val and test refer:

run_adni_preprocess_val.sh

and

run_adni_preprocess_test.sh

Examples in the preprocessed dataset

Here are some examples of scans for each categories in our test dataset:

Neural Network Training

Train the network ADNI dataset:

python main.py

You can create your own config files and add a --config flag to indicate the name of your config files.

Model Evaluation

We provide the evaluation code in Model_eval.ipynb, where you can load and evaluate our trained model. The trained best model (with widening factor 8 and adding age) can be found here.

Results

Method Acc. Balanced Acc. Micro-AUC Macro-AUC
ResNet-18 3D 52.4% 53.1% - -
AlexNet 3D 57.2% 56.2% 75.1% 74.2%
X 1 56.4% 54.8% 74.2% 75.6%
X 2 58.4% 57.8% 77.2% 76.6%
X 4 63.2% 63.3% 80.5% 77.0%
X 8 66.9% 67.9% 82.0% 78.5%
X 8 + age 68.2% 70.0% 82.0% 80.0%

References

@inproceedings{liu2020design,
  title={On the design of convolutional neural networks for automatic detection of Alzheimer’s disease},
  author={Liu, Sheng and Yadav, Chhavi and Fernandez-Granda, Carlos and Razavian, Narges},
  booktitle={Machine Learning for Health Workshop},
  pages={184--201},
  year={2020},
  organization={PMLR}
}

@article{liu2022generalizable,
  title={Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs},
  author={Liu, Sheng and Masurkar, Arjun V and Rusinek, Henry and Chen, Jingyun and Zhang, Ben and Zhu, Weicheng and Fernandez-Granda, Carlos and Razavian, Narges},
  journal={Scientific Reports},
  volume={12},
  number={1},
  pages={1--12},
  year={2022},
  publisher={Nature Publishing Group}
}