MADE-for-ASD

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Codebase of 'MADE-for-ASD: A Multi-Atlas Deep Ensemble Network for Diagnosing Autism Spectrum Disorder'

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# MADE-for-ASD: A Multi-Atlas Deep Ensemble Network for Detecting Autism Spectrum Disorder

This repository contains the code of our above-titled paper, published in the "Computers in Biology and Medicine" journal. **Codes were primarily developed by [Xuehan Liu](https://github.com/lxhwww)**.

Paper link: [[ScienceDirect](https://doi.org/10.1016/j.compbiomed.2024.109083)] [[arXiv](https://arxiv.org/abs/2407.07076)] [[PDF](https://hasan-rakibul.github.io/pdfs/liu2024made.pdf)]

**Author**: Xuehan Liu*, Md Rakibul Hasan*, Tom Gedeon and Md Zakir Hossain <br>
(*Equal contribution)

**Abstract:** In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), we aim to bridge the gap between traditional, time-consuming diagnostic methods and potential automated solutions. To this end, we propose a multi-atlas deep ensemble network, **MADE-for-ASD**, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. We further integrate demographic information into the prediction workflow, which enhances ASD detection accuracy and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available Autism Brain Imaging Data Exchange (ABIDE), consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves an accuracy of 75.2% on the whole dataset and 96.40% on a subset – both surpassing the reported ASD detection performances in the literature. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis.

**Disclaimer:** While we have made every effort to ensure the completeness of this codebase, some components (implementation of k-fold cross-validation and weighted voting mechanism) are missing. We, the co-authors, have actively tried to contact the first author, [Xuehan Liu](https://github.com/lxhwww), to obtain the most up-to-date version of the code once we detected the issue. Unfortunately, our efforts were unsuccessful, as her university email is no longer active following her graduation. We will promptly update this repository should we receive any further information. To get the missing bits, we recommend reaching out to her directly if possible in any way.

## Environment Setup

Please be aware that this code is meant to be run with Python 3 under Linux (MacOS may work, Windows probably not). Download the packages from `requirements.txt`:

```bash
python -m pip install -r requirements.txt
```
We use the [ABIDE I dataset](http://fcon_1000.projects.nitrc.org/indi/abide/). We use the pre-processing method from the [preprocessed-connectomes-project](https://github.com/preprocessed-connectomes-project/abide).

## Run Experiments

We put all the experiment command and the result in the `Experiments.ipynb` file. As an example, we prepare the hdf5 files in advance of the CC200, AAL, EZ atlas data from the NYU site, and show the entire experiment on this data as followings:

1. Run `download_abide.py` to download the raw data.

We use the command below:

```bash
#download_abide.py [--pipeline=cpac] [--strategy=filt_global] [<derivative> ...]
python download_abide.py
```

2. To show the demographic Information of ABIDE I.

```bash
python pheno_info.py
```

3. Run `prepare_data.py` to compute the correlation. Then we can get the hdf5 files. The dataset (hdf5) can be downloaded from [link](https://drive.google.com/file/d/1-WyQ7IOqSxaGcoA6MR4ydJdazlqzKYMY/view?usp=drive_link); you need to put it in the "data" folder. Or you can simply run the code as:

```bash
#download_abide.py [--folds=N] [--whole] [--male] [--threshold] [--leave-site-out] [--NYU-site-out] [<derivative> ...]

python prepare_data.py --folds=10 --whole cc200 aal ez
```

4. Using Stacked Sparse Denoising Autoencoder (SSDAE) to perform Multi-atlas Deep Feature Representation, and using multilayer perceptron (MLP) and ensemble learning to classify the ASD and TC.

```bash
#nn.py [--whole] [--male] [--threshold] [--leave-site-out] [<derivative> ...]

rm ./data/models/*mlp*
python nn.py --whole cc200 aal ez
```

5. Evaluating the MLP model on test dataset. You can use the saved models from provious or download the models from the [link](https://drive.google.com/drive/folders/1rIZpXdafzI-nb0YonkL0XQs6pOSSxRUf?usp=drive_link), and **can run this command directly without running previous steps**.

```bash
#nn_evaluate.py [--whole] [--male] [--threshold] [--leave-site-out] [--NYU-site-out] [<derivative> ...]

python nn_evaluate.py --whole cc200 aal ez
```
        
## Quality Analysis and Visualisation

All the analysis for the subset and the visualisation of top ROIs, which mentioned in our work, are included in the `analysis_and_visulaisation.ipynb` file.

## Citation
If you find this repository useful in your research, please cite our paper:
```bibtex
@article{liu2024made,
    title = {{MADE}-for-{ASD}: A Multi-Atlas Deep Ensemble Network for Diagnosing Autism Spectrum Disorder},
    author = {Xuehan Liu and Md Rakibul Hasan and Tom Gedeon and Md Zakir Hossain},
    journal = {Computers in Biology and Medicine},
    volume = {182},
    pages = {109083},
    year = {2024},
    issn = {0010-4825},
    doi = {10.1016/j.compbiomed.2024.109083}
}
```

MADE-for-ASD

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