Interpretable Classification of Alzheimer's Disease Pathologies with a Convolutional Neural Network Pipeline
This repository accompanies the publication above. Specifically, we include Jupyter notebooks to reproduce all image preprocessing and processing, training of convolutional neural networks, confidence visualizations, and saliency maps. The available code is provided as-is, and does not constitute a full-fledged software package for analysis.
This code repository was developed on Linux CentOS 7 and Ubuntu 18 and has not been tested on on other systems (Windows, MacOS).
Code requires the following Python packages:
python 3.6.5 hc3d631a_2 ipython 6.4.0 py36_0 jupyter 1.0.0 py36_4 matplotlib 2.2.2 py36h0e671d2_1 numpy 1.14.3 py36hcd700cb_1 pandas 0.23.0 py36h637b7d7_0 scikit-learn 0.19.1 py36h7aa7ec6_0 scikit-image 0.13.1 py36h14c3975_1 scipy 1.1.0 py36hfc37229_0 pytorch 0.3.0 py35_cuda8.0.61_cudnn7.0.3hb362f6e_4 pytorch torchvision 0.2.1 py36_1 pytorch libopencv 3.4.1 h1a3b859_1 opencv 3.4.1 py36h6fd60c2_2 py-opencv 3.4.1 py36h0676e08_1 pyvips 2.1.2 <pip> tqdm 4.23.4 py36_0
In addition, libvips (version 8.2.2-1) was used in this study for handling of whole slide images. The open-source python package pyvips is a wrapper for libvips ((https://jcupitt.github.io/libvips/), which can be installed in Linux with the following:
sudo apt-get install libvips
Graphics Cards for Deep Learning - All deep learning models were trained using 4 X NVIDIA 1080 GPUs. As indicated above, PyTorch requires CUDA 8.0 and cuDNN 7.0 for compatibility.
We recommend creating a new Anaconda (https://www.anaconda.com/) environment with the dependencies above.
This repository can be cloned directly through:
git clone https://github.com/keiserlab/plaquebox-paper.git
Notebook 2.2) CNN Models - Test Cases is a demo that shows how to apply the trained CNN model on unseen dataset. Simply download the tiles from Zenodo repository and unzip it to the /data folder, then the notebook can be run through Jupyter.
This repository contains 11 notebooks to reproduce the results from the linked paper. Each notebook includes details relevant to a portion of the described pipeline, with detailed descriptions at the top of each notebook. For results reproduction, these files are presented in sequential order and depend on the previous notebook.
Before running the code, it is necessary to download the raw datafiles from the corresponding Zenodo repository above and unzip the files to the /data folder.
The filepaths must be specified as indicated in each notebook to specify the location of the downloaded data.
Notebooks 1.1-1.3 describe necessary preprocessing steps, including: color normalization, whole slide image tiling, blob detection, and dataset splitting.
Notebooks 2.1 and 2.2 detail model development, training, and testing.
Notebook 3 describes prediction confidence heatmaps.
Notebooks 4.1 and 4.2 describe feature interpretation studies, including feature occlusion and guided-grad cam studies.
Notebooks 5.1-5.3 describe whole slide scoring.