无需标注数据，自动适配不同医院3D医学影像分割。
Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images
- 用源域数据估计分布，生成伪标签自训练。
- 在真实3D医学数据集上达到当前最优性能。
- 适合无标注资源且需跨医院数据适配的场景。
3D医学图像分割的手动标注费时费力，且数据隐私限制了众包标注的应用。为此，我们提出一种新的无源无监督域适应方法。核心思想是通过基础模型估计相关源域的内部学习分布，并生成伪标签，用于模型自我训练以优化性能。实验表明，该方法在真实世界3D医学图像数据集上取得了当前最优表现。
原文摘要 · Abstract (English)
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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