Search
Abstract
We are increasingly striving to introduce modern artificial intelligence techniques in medicine and elevate medical care, catering to both patients and specialists. An essential aspect that warrants concurrent development is the protection of personal data, especially with technology's advancement, along with addressing data disparities to ensure model efficacy. This study assesses various domain adaptation techniques and federated learning to determine optimal integration strategies for enhanced security and the challenges posed by diverse datasets. Experiments utilized deep learning models, three domain adaptation methods, and a federated learning framework, focusing on mammography imaging for breast cancer detection. Results indicate a notable improvement of up to 20% with domain adaptation and an additional 10% with federated learning integration.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Erimus Ł., Borowska A., Jaromin A., Lewko A., Rumiński J.: Data Domain Adaptation in Federated Learning in the Breast Mammography Image Classification Problem// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi61632.2024.10613534
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
seen 7 times