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Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes and Kerstin Ritter

Our paper was presented on the MLCN workshop at MICCAI 2018 in Granada (Slides).


Abstract: Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer’s disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network’s classification decisions by highlight- ing relevant areas in the input image. We compare the methods qualita- tively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer’s disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed rel- evance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.



You can use the visualization methods in this repo on your own model (PyTorch; for other frameworks see below) like this:

from interpretation import sensitivity_analysis
from utils import plot_slices

cnn = load_model()
mri_scan = load_scan()

heatmap = sensitivity_analysis(cnn, mri_scan, cuda=True)
plot_slices(mri_scan, overlay=heatmap)

heatmap is a numpy array containing the relevance heatmap. The methods should work for 2D and 3D images alike. Currently, four methods are implemented and tested: sensitivity_analysis, guided_backprop, occlusion, area_occlusion. There is also a rough implementation of grad_cam, which seems to work on 2D photos, but not on brain scans. Please look at for further documentation.

Code Structure

The codebase uses PyTorch and Jupyter notebooks. The main files for the paper are:

  • training.ipynb is the notebook to train the model and perform cross validation.
  • interpretation-mri.ipynb contains the code to create relevance heatmaps with different visualization methods. It also includes the code to reproduce all figures and tables from the paper.
  • All *.py files contain methods that are imported in the notebooks above.

Additionally, there are two other notebooks:

  • interpretation-photos.ipynb uses the same visualization methods as in the paper but applies them to 2D photos. This might be an easier introduction to the topic.
  • small-dataset.ipynb contains some old code to run a similar experiment on a smaller dataset.

Trained Model and Heatmaps

If you don't want to train the model and/or run the computations for the heatmaps yourself, you can just download my results: Here is the final model that I used to produce all heatmaps in the paper (as a pytorch state dict; see paper or code for more details on how the model was trained). And here are the numpy arrays that contain all average relevance heatmaps (as a compressed numpy .npz file). Please have a look at interpretations-mri.ipynb for instructions on how to load and use these files.


The MRI scans used for training are from the Alzheimer Disease Neuroimaging Initiative (ADNI). The data is free but you need to apply for access on Once you have an account, go here and log in.


We included csv tables with metadata for all images we used in this repo (data/ADNI/ADNI_tables). These tables were made by combining several data tables from ADNI. There is one table for 1.5 Tesla scans and one for 3 Tesla scans. In the paper, we trained only on the 1.5 Tesla images.


To download the corresponding images, log in on the ADNI page, go to "Download" -> "Image Collections" -> "Data Collections". In the box on the left, select "Other shared collections" -> "ADNI" -> "ADNI1:Annual 2 Yr 1.5T" (or the corresponding collection for 3T) and download all images. We preprocessed all images by non-linear registration to a 1 mm isotropic ICBM template via ANTs with default parameters, using the quick registration script from here.

To be consistent with the codebase, put the images into the folders data/ADNI/ADNI_2Yr_15T_quick_preprocessed (for the 1.5 Tesla images) or data/ADNI/ADNI_2Yr_3T_preprocessed (for the 3 Tesla images). Within these folders, each image should have the following path: <PTID>/<Visit (spaces removed)>/<PTID>_<Scan.Date (/ replaced by -)>_<Visit (spaces removed)>_<Image.ID>_<DX>_Warped.nii.gz. If you want to use a different directory structure, you need to change the method get_image_filepath and/or the filenames in

Users from Ritter/Haynes lab

If you're working in the Ritter/Haynes lab at Charité Berlin, you don't need to download any data, but simply uncomment the correct ADNI_DIR variable in


  • Python 2 (mostly compatible with Python 3 syntax, but not tested)
  • Scientific packages (included with anaconda): numpy, scipy, matplotlib, pandas, jupyter, scikit-learn
  • Other packages: tqdm, tabulate
  • PyTorch: torch, torchvision (tested with 0.3.1, but mostly compatible with 0.4)
  • torchsample: I made a custom fork of torchsample which fixes some bugs. You can download it from or install directly via pip install git+ Please use this fork instead of the original package, otherwise the code will break.

Non-pytorch Models

If your model is not in pytorch, but you still want to use the visualization methods, you can try to transform the model to pytorch (overview of conversion tools).

For keras to pytorch, I can recommend nn-transfer. If you use it, keep in mind that by default, pytorch uses channels-first format and keras channels-last format for images. Even though nn-transfer takes care of this difference for the orientation of the convolution kernels, you may still need to permute your dimensions in the pytorch model between the convolutional and fully-connected stage (for 3D images, I did x = x.permute(0, 2, 3, 4, 1).contiguous()). The safest bet is to switch keras to use channels-first as well, then nn-transfer should handle everything by itself.


If you use our code, please cite our paper:

  title={Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease},
  author={Rieke, Johannes and Eitel, Fabian and Weygandt, Martin and Haynes, John-Dylan and Ritter, Kerstin},
  booktitle={Machine Learning in Clinical Neuroimaging (MLCN)},