An implementation of the Cambridge parameter efficient 3D convolutional network for the classification of Alzheimer's disease in pytorch. The architecture is described in detail in this link. More specifically the architecture can classify between static and progressive Mild cognitive impairement which is a disease that precludes Alzheimers. The area under the curve achieved for this harder second task is: 0.88.
You can read more about it, including the data required, in this medium link.
this link of dissertation.
General code was developed by Daniel Wilde link
- Neural Net
- 3D seperable convolution to reduce number of parameters and hence overfitting.
- Multiple concurrent data streams
- MRI Data
- Clinical Data
- Program
- Saves and loads model
- Supports k-fold cross validation
- Logs the train and test loss
- Outputs Area under the ROC curve for each k-fold
- Saves each model under a unique identifier
The following requirements are contained within the requirements.yml file. To install them just run:
$ conda env create -f environment.yml
| Library | Version | Purpose |
|---|---|---|
| Pytorch | 1.4.0 | Deep learning library for tensor transformations |
| Torchvision | 0.2.2 | library required by pytorch |
| Numpy | 1.18.1 | Datascience library for creating and manipulating arrays in python |
| sklearn | 0.23 | Machine learning library used to output area under the curve and other stats. |
| matplotlib | 3.1.2 | A visualization library required by sklearn to generate the 2D graphs. |
| Pandas | 0.25.3 | Datascience library for working with tabular data |
| Nibabel | 3.0.0 | Library for loading in MRI scans into python |
| Tqdm | 4.42.1 | library for outputting progress to the console. |
You must place the data folder in a directory above the project folder. The data folder must contain a csv file with the clinical data and a folder containing the MRI images for each class. The data can be obtained from the ADNI website. The data requirements are described in more detail in the wiki but to sumarize the clinical vector must be of length 21 and the mri scans have to be of dimension 110x110x110. Alternatively you can download a zip from this link of my preprocessed dataset.
- Place your data in the directory above. (See wiki)
- Run "python train_model.py"
- Wait about 20 mins; grab some snacks.
- Look in the save folder for logs and graphs (..\graphs, ..\logs).
Once the model has been trained and you are happy with its perfomance after reviewing the graphs and logs you can use it for predictions. This is also known as inference and can be initiated by running the interface.py script. This script will give you an option to load a model of your choice and perform inference on the specified data.
To read an explanation on this checkout this medium article. https://medium.com/@hextra_19712/deep-learning-for-alzheimers-classification-57611161e442?sk=ebc8035cead352f51f7d0e105a69a6c7
!(image)[https://i.gyazo.com/1b655be7b33cff82372088abdbf8eb4c.png]
