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PyTorch implementation of VAGAN: Visual Feature Attribution Using Wasserstein GANs

This code aims to reproduce results obtained in the paper "Visual Feature Attribution using Wasserstein GANs" (official repo, TensorFlow code)

Description

This repository contains the code to reproduce results for the paper cited above, where the authors presents a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN). The code works for both synthetic (2D) and real 3D neuroimaging data, you can check below for a brief description of the two datasets.

anomaly maps examples

Here is an example of what the generator/mapper network should produce: ctrl-click on the below image to open the gifv in a new tab (one frame every 50 iterations, left: input, right: anomaly map for synthetic data at iteration 50 * (its + 1)).

anomaly maps examples

Synthetic Dataset

"Data: In order to quantitatively evaluate the performance of the examined visual attribution methods, we generated a synthetic dataset of 10000 112x112 images with two classes, which model a healthy control group (label 0) and a patient group (label 1). The images were split evenly across the two categories. We closely followed the synthetic data generation process described in [31][SubCMap: Subject and Condition Specific Effect Maps] where disease effects were studied in smaller cohorts of registered images. The control group (label 0) contained images with ran- dom iid Gaussian noise convolved with a Gaussian blurring filter. Examples are shown in Fig. 3. The patient images (label 1) also contained the noise, but additionally exhib- ited one of two disease effects which was generated from a ground-truth effect map: a square in the centre and a square in the lower right (subtype A), or a square in the centre and a square in the upper left (subtype B). Importantly, both dis- ease subtypes shared the same label. The location of the off-centre squares was randomly offset in each direction by a maximum of 5 pixels. This moving effect was added to make the problem harder, but had no notable effect on the outcome."

image

ADNI Dataset

Currently we only implemented training on synthetic dataset, we will work on implement training on ADNI dataset asap (but pull requests are welcome as always), we put below ADNI dataset details for sake of completeness.

"We selected 5778 3D T1-weighted MR images from 1288 subjects with either an MCI (label 0) or AD (label 1) diagnosis from the ADNI cohort. 2839 of the images were acquired using a 1.5T magnet, the remainder using a 3T magnet. The subjects are scanned at regular intervals as part of the ADNI study and a number of subjects converted from MCI to AD over the years. We did not use these cor- respondences for training, however, we took advantage of it for evaluation as will be described later. All images were processed using standard operations available in the FSL toolbox [52][Advances in functional and structural MR image analysis and implementation as FSL.] in order to reorient and rigidly register the images to MNI space, crop them and correct for field inhomogeneities. We then skull-stripped the images using the ROBEX algorithm [24][Robust brain extraction across datasets and comparison with publicly available methods]. Lastly, we resampled all images to a resolution of 1.3 mm 3 and nor- malised them to a range from -1 to 1. The final volumes had a size of 128x160x112 voxels."

"Data used in preparation of this article were obtained from the Alzheimers disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf"

Usage

Training

To train the WGAN on this task, cd into this repo's src root folder and execute:

$ python train.py

This script takes the following command line options:

Running the command without arguments will train the models with the default hyperparamters values (producing results shown above).

Models

We ported all models found in the original repository in PyTorch, you can find all implemented models here: https://github.com/orobix/Visual-Feature-Attribution-Using-Wasserstein-GANs-Pytorch/tree/master/src/models

Useful repositories and code

.bib citation

cite the paper as follows (copied-pasted it from arxiv for you):

@article{DBLP:journals/corr/abs-1711-08998,
  author    = {Christian F. Baumgartner and
               Lisa M. Koch and
               Kerem Can Tezcan and
               Jia Xi Ang and
               Ender Konukoglu},
  title     = {Visual Feature Attribution using Wasserstein GANs},
  journal   = {CoRR},
  volume    = {abs/1711.08998},
  year      = {2017},
  url       = {http://arxiv.org/abs/1711.08998},
  archivePrefix = {arXiv},
  eprint    = {1711.08998},
  timestamp = {Sun, 03 Dec 2017 12:38:15 +0100},
  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1711-08998},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

License

This project is licensed under the MIT License

Copyright (c) 2018 Daniele E. Ciriello, Orobix Srl (www.orobix.com).