@inproceedings{047f6de2e35d4862af6cf3a1dea27075,
title = "Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity",
abstract = "This paper tackles the challenging problem of medical site adaptation; i.e., learning a model from multi-site source data such that it can be modified and adapted to a new site using only unlabeled data from the new site. The method is based on Domain Specific Batch Normalization architecture and uses the Batch Normalization statistics of the new site to find the most similar internal site. The similarity measure is computed in an embedded space of the BN parameters. We evaluated our method on the task of MRI prostate segmentation. Public datasets from six different institutions were used, containing distribution shifts. The experimental results show that the proposed approach outperforms other generalization and adaptation methods.",
keywords = "batch-normalization, domain adaptation, multi-site, prostate segmentation",
author = "{Kasten Serlin}, Shira and Jacob Goldberger and Hayit Greenspan",
note = "Funding Information: This research was supported by the Ministry of Science & Technology, Israel. Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 ; Conference date: 28-03-2022 Through 31-03-2022",
year = "2022",
doi = "10.1109/ISBI52829.2022.9761487",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "ISBI 2022 - Proceedings",
address = "United States",
}