iolag/upd_study

This repository contains code from our comparative study on state of the art unsupervised pathology detection and segmentation methods.

upd_study's Issues

Performance on the MVTec dataset

Hello,

Have you evaluated these methods using the MVTec dataset? I attempted to use your code on the MVTec dataset, but the outcomes were significantly inferior compared to those documented in the original paper. Specifically, the AUC for most methods was about 0.5-0.8 after 2000 training steps, except the method RD and DFR.

I used the following command. I changed the small testing set in your code to the complete testing set and observed the anom_val/sample-auroc in the wandb.
UPD_study/models/H-TAE-S/HTAEStrainer.py --shuffle t -ev t -mod MyData --percentage 100 --eval False --seed 10 --val_frequency 200 --log_frequency 200 --anom_val_frequency 200

Is there any problem with my usage? Look forward to your reply.

Thank you in advance!

Questions about computing dice

Thanks for your great work. I have a question about computing dice in this code.

In utilies/metrics.py, function compute_best_dice() concatenates all the testing samples together to compute the dice score, instead of computing the dice of each sample one by one.

I am wondering whether this operation is reasonable as the dice score is usually caculated for each case seperately. Thanks.

Path to dataset

Hi,

This is a great work! Could you please tell me the direct path to the cheXPert dataset? I cannot find the downsampled one in the size about 11G, but only the fullsize one (400G+) here https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2.

Thank you very much for your help!

Best,
Cathy