@inproceedings{dc81f95f06c44012903c789df7d64abf,
title = "Uncertainty Learning towards Unsupervised Deformable Medical Image Registration",
abstract = "Uncertainty estimation in medical image registration enables surgeons to evaluate the operative risk based on the trustworthiness of the registered image data thus of paramount importance for practical clinical applications. Despite the recent promising results obtained with deep unsupervised learning-based registration methods, reasoning about uncertainty of unsupervised registration models remains largely unexplored. In this work, we propose a predictive module to learn the registration and uncertainty in correspondence simultaneously. Our framework introduces empirical randomness and registration error based uncertainty prediction. We systematically assess the performances on two MRI datasets with different ensemble paradigms. Experimental results highlight that our proposed framework significantly improves the registration accuracy and uncertainty compared with the baseline.",
keywords = "Accountability, Explainable AI, Fairness, Privacy and Ethics in Vision Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy",
author = "Xuan Gong and Luckyson Khaidem and Wentao Zhu and Baochang Zhang and David Doermann",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 ; Conference date: 04-01-2022 Through 08-01-2022",
year = "2022",
doi = "10.1109/WACV51458.2022.00162",
language = "English",
series = "Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1555--1564",
booktitle = "Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022",
address = "United States",
}