Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity

Shira Kasten Serlin; Jacob Goldberger; Hayit Greenspan

doi:10.1109/isbi52829.2022.9761487

Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity

Shira Kasten Serlin, Jacob Goldberger, Hayit Greenspan

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	ISBI 2022 - Proceedings
Subtitle of host publication	2022 IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665429238
DOIs	https://doi.org/10.1109/isbi52829.2022.9761487
State	Published - 2022
Event	19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India Duration: 28 Mar 2022 → 31 Mar 2022

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2022-March
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Country/Territory	India
City	Kolkata
Period	28/03/22 → 31/03/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

batch-normalization
domain adaptation
multi-site
prostate segmentation

Access to Document

10.1109/isbi52829.2022.9761487

Cite this

Kasten Serlin, S., Goldberger, J., & Greenspan, H. (2022). Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity. In ISBI 2022 - Proceedings: 2022 IEEE International Symposium on Biomedical Imaging (Proceedings - International Symposium on Biomedical Imaging; Vol. 2022-March). IEEE Computer Society. https://doi.org/10.1109/isbi52829.2022.9761487

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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",

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doi = "10.1109/isbi52829.2022.9761487",

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Kasten Serlin, S, Goldberger, J & Greenspan, H 2022, Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity. in ISBI 2022 - Proceedings: 2022 IEEE International Symposium on Biomedical Imaging. Proceedings - International Symposium on Biomedical Imaging, vol. 2022-March, IEEE Computer Society, 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022, Kolkata, India, 28/03/22. https://doi.org/10.1109/isbi52829.2022.9761487

Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity. / Kasten Serlin, Shira; Goldberger, Jacob; Greenspan, Hayit.
ISBI 2022 - Proceedings: 2022 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2022. (Proceedings - International Symposium on Biomedical Imaging; Vol. 2022-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity

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