f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Code for reproducing f-AnoGAN training and anomaly scoring presented in "f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks" (accepted manuscript). This work extends AnoGAN: "Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery".

Referencing and citing f-AnoGAN

If you use (parts of) this code in your work please refer to this citation:

Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U., 2019. f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks. Medical Image Analysis 54, 30-44. DOI: https://doi.org/10.1016/j.media.2019.01.010

Prerequisites

• Python (2.7), TensorFlow (1.2), NumPy, SciPy, Matplotlib
• A recent NVIDIA GPU

f-AnoGAN building blocks

• wgangp_64x64.py

  : Training a 64x64 WGAN architecture yields a trained generator (G) and discriminator (D). Modifies and extends (including

  tflib/

  ) Ishaan Gulrajani's Tensorflow implementation of the WGAN-GP model proposed in "Improved Training of Wasserstein GANs" (GitHub).

• z_encoding_izif.py

  : Training the izi_f encoder (E) based on the trained WGAN model (G and D). Please refer to the full paper for more detailed information.

• anomaly_detection.py

  : Code for anomaly scoring utilizing the trained G, D, and E.

Setting (image) paths

Image paths are set in

tflib/img_loader.py

trainset_path

trainset_val_path

test_normal_path

test_anom_path

Please edit that file to specify the paths to your datasets.

Misc

Results of related research work are provided at CIR.