Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images

Ruitong Sun; Mohammad Rostami

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arXiv · 2410.09210

Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images

Ruitong Sun·Mohammad Rostami

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Abstract

Manual annotation of 3D medical images for segmentation tasks is tedious and time-consuming. Moreover, data privacy limits the applicability of crowd sourcing to perform data annotation in medical domains. As a result, training deep neural networks for medical image segmentation can be challenging. We introduce a new source-free Unsupervised Domain Adaptation (UDA) method to address this problem. Our idea is based on estimating the internally learned distribution of a relevant source domain by a base model and then generating pseudo-labels that are used for enhancing the model refinement through self-training. We demonstrate that our approach leads to SOTA performance on a real-world 3D medical dataset.

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BibTeX RIS

Ruitong Sun, Mohammad Rostami. 2024-10-11. Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images. https://arxiv.org/abs/2410.09210

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