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

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

2. 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

• Sheng Liu, Chhavi Yadav, Carlos Fernandez-Granda, Narges Razavian. "On the design of convolutional neural networks for automatic detection of Alzheimer’s disease", in NeurIPS ML4H, 2019.