@inproceedings{2c9764f6071e4a6a9953e02a42e78767,
title = "PLPP: A Pseudo Labeling Post-Processing Strategy for Unsupervised Domain Adaptation",
abstract = "A well known problem in medical imaging is the ability to use an existing model learned on source data, in a new site. This is known as the domain shift problem. We propose a pseudo labels procedure, which was originally introduced for semi-supervised learning, that is suitable for unsupervised domain adaptation (UDA). We iteratively improve the pseudo labels of the target domain data only using the current pseudo labels without involving the labeled source domain data. We applied our method to several medical MRI image segmentation tasks. We show that, by combining our approach as a post-processing step in standard UDA algorithms, we consistently and significantly improve the segmentation results on test images from the target site.",
keywords = "pseudo labels, site adaptation, transfer learning, unsupervised domain adaptation",
author = "Natan, {Tomer Bar} and Hayit Greenspan and Jacob Goldberger",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1109/ISBI53787.2023.10230628",
language = "אנגלית",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "ארצות הברית",
}