ishaanb92 / pytorch_probunet

Probabilistic U-Net with MC-Dropout

Probabilistic U-Net with MC-Dropout

The code in this repository was developed for the QUBIQ 2021 challenge. We extend the Probabilistic U-Net to estimate model uncertainty (in addition to data uncertainty) using the popular MC-Dropout technique. This approach is inspired from Hu et al., 2019 that uses variational dropout within the Probabilistic U-Net framework to estimate the model uncertainty.

The code for the Probabilistic U-Net has been forked from here. We have restructured the code to enable import as an external module, in addition to changes to handle multi-channel images and uncertainty estimation using MC-Dropout.

UPDATE: Supports low-rank approximation for prior and posterior covariance matrices to capture more expressive distributions. See Monteiro et al. (2020) for background on low-rank approximation of covariance matrices of multivariate Gaussian distributions.

Usage:

Follow these steps to use this model in your project:

• Clone this repository into a local folder git clone https://github.com/kilgore92/PyTorch_ProbUNet.git

• Install the model in your python environment python setup.py install

• Import the model into your script from probabilistic_unet.model import ProbabilisticUnet

If you have any questions, please open a pull-request or issue and I will get back to you.

pytorch_probunet's Issues

Code exploitation

Thank you for the great idea, please How to use this code to calculate model uncertainty?