Unsupervised Anomaly Localization using Variational Auto-Encoders
Copyright © German Cancer Research Center (DKFZ), Division of Medical Image Computing (MIC). Please make sure that your usage of this code is in compliance with the code license.
Code for the MICCAI19 paper Unsupervised Anomaly Localization using Variational Auto-Encoders.
Abstract:
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious. Currently, however, the reconstruction-based localization by design requires adjusting the model architecture to the specific problem looked at during evaluation. This contradicts the principle of building assumption-free models. We propose complementing the localization part with a term derived from the Kullback-Leibler (KL)-divergence. For validation, we perform a series of experiments on FashionMNIST as well as on a medical task including >1000 healthy and >250 brain tumor patients. Results show that the proposed formalism outperforms the state of the art VAE-based localization of anomalies across many hyperparameter settings and also shows a competitive max performance.
How to run:
The Fashion-MNIST experiments can simply be reproduced using the minst_script.py.
For the brain MRI experiments first download the HCP and BraTS-17 datasets and preprocess them using utils/preprocess_brain.py. Then you can reproduce the results using the brain_script.py script.