Accepted in MICCAI-MLMI-2020
More details here: MICCAI-MLMI-2020
Folder Structures:
- dataset_groups: It holds various datasets with their respective processing code in it.
- projects: It holds various bayesian architectures i.e. 4 that we used for our experiments. Fully Bayesian, Quicknat with dropout, Probabilistic U-Net, Hierarchical U-Net.
- interface: It has all the base class for solver, data processing pipeline, evaluator and run setup. Provides a consistent platform for all projects to train and evaluate.
- utils: To have utility functions like logger & notifier to mention a few.
- stat_analysis: This folder contains post segmentation data analysis with diesease classification and group analysis stuff using python and R toolkit.
if you like the paper, and willing to extend the work, please cite:
@inproceedings{senapati2020bayesian,
title={Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers},
author={Senapati, Jyotirmay and Roy, Abhijit Guha and P{\"o}lsterl, Sebastian and Gutmann, Daniel and Gatidis, Sergios and Schlett, Christopher and Peters, Anette and Bamberg, Fabian and Wachinger, Christian},
booktitle={International Workshop on Machine Learning in Medical Imaging},
pages={270--280},
year={2020},
organization={Springer}
}
- Jyotirmay Senapati - jyotirmay-senapati