The project page is here.
This is the official code repository for Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks.
In this work, we present a simple yet effective Bayesian neural network architecture to estimate the inter-rater uncertainty in medical image segmentation.
Please download the dataset from here
This dataset is annotated by Zhiheng Zhang, Jan Kirschke and Benedikt Wiestler.
Huge shout for their contribution to this work!!!
The train and validation data could be obtained here
The preprocessed LIDC-IDRI dataset can be downloaded from the bucket from Deepmind.
To reproduce our results on LIDC-IDRI dataset, please run:
bash cli/boemd/train_bomd_lidc_patient.sh
Our work is released under the MIT license. Please check the LICENSE for more information.
If you find our work is helpful for your research, please cite our code repository in your work.
@misc{hu2023interrater,
title={Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks},
author={Qingqiao Hu and Hao Wang and Jing Luo and Yunhao Luo and Zhiheng Zhangg and Jan S. Kirschke and Benedikt Wiestler and Bjoern Menze and Jianguo Zhang and Hongwei Bran Li},
year={2023},
archivePrefix={arXiv},
}
Phiseg code is based on Phiseg-code and built upon UNet-Zoo.
Probabilistic Unet code is based on probabilistic_unet, Probabilistic-Unet-Pytorch and UNet-Zoo.
Bayesian CNN is based on PyTorch-BayesianCNN.
Bayes by Backprop is based on Bayesian-Neural-Networks.
Preprocessed patient-id specific LICD-IDRI dataset is from hierarchical_probabilistic_unet.