orobix/Visual-Feature-Attribution-Using-Wasserstein-GANs-Pytorch

This code aims to reproduce results obtained in the paper "Visual Feature Attribution using Wasserstein GANs" 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. 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)). "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 Figure 1. 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."