Abstract
This study addresses the challenge of medical image segmentation when transferring a pre-trained model from one medical site to another without access to pre-existing labels. The method involves utilizing a self-training approach by generating pseudo-labels of the target domain data. To do so, a strategy that is based on a smooth transition between domains is implemented where we initially feed easy examples to the network and gradually increase the difficulty of the examples. To identify the level of difficulty, we use a binary classifier trained to distinguish between the two domains by considering that target images easier if they are classified as source examples. We demonstrate the improved performance of our method on a range of medical MRI image segmentation tasks. When integrating our approach as a post-processing step in several standard Unsupervised Domain Adaptation (UDA) algorithms, we consistently observed significant improvements in the segmentation results on test images from the target site.
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Notes
- 1.
Code for our implementation including data pre processing and trained models is available at: https://github.com/TomerBarNatan/PLST.
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Bar Natan, T., Greenspan, H., Goldberger, J. (2024). PLST: A Pseudo-labels with a Smooth Transition Strategy for Medical Site Adaptation. In: Koch, L., et al. Domain Adaptation and Representation Transfer. DART 2023. Lecture Notes in Computer Science, vol 14293. Springer, Cham. https://doi.org/10.1007/978-3-031-45857-6_4
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