Constrained Unsupervised Anomaly Segmentation of Brain Lesions

This repository contains code for unsupervised anomaly segmentation in brain lesions. Specifically, the implemented methods aim to constrain the optimization process to force a VAE to homogenize the activations produced in normal samples.

If you find these methods useful for your research, please consider citing:

J. Silva-Rodríguez, V. Naranjo and J. Dolz, "Looking at the whole picture: constrained unsupervised anomaly segmentation", in British Machine Vision Conference (BMVC), 2021. (paper)(conference)

J. Silva-Rodríguez, V. Naranjo and J. Dolz, "Constrained unsupervised anomaly segmentation", Medical Image Analysis, vol. 80, p. 102526, 2022. (paper)

GRADCAMCons: looking at the whole picture via size constraints

python main.py --dir_out ../data/results/gradCAMCons/ --method gradCAMCons --learning_rate 0.00001 --wkl 1 --wae 1000 --t 10

AMCons: entropy maximization on activation maps

python main.py --dir_out ../data/results/AMCon/ --method camCons --learning_rate 0.0001 --wkl 10 --wH 0.1

Visualizations

Contact

For further questions or details, please directly reach out Julio Silva-Rodríguez (jusiro95@gmail.com)