用正负样本学习法，为每张医学图像自适应选伪标签。
Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learning
- 基于正负样本学习，自动判断图像中前景与背景区域。
- 在未标注图像上实现精准伪标签选择，提升分割效果。
- 适合缺乏标注数据的医学图像分割任务。
本文提出一种针对医学图像分割的新型伪标签方法，可对单个图像进行学习以选择有效的伪标签。引入仅含正样本和未标注样本的正负样本学习（Positive and Unlabeled Learning, PU learning）框架，用于二分类问题，从而为每张未标注图像构建合适的度量，以区分前景与背景区域。该方法使伪标签选择更灵活，尤其适用于复杂多变的背景区域。实验结果验证了该方法的有效性。
原文摘要 · Abstract (English)
This paper proposes a novel pseudo-labeling method for medical image segmentation that can perform learning on ``individual images'' to select effective pseudo-labels. We introduce Positive and Unlabeled Learning (PU learning), which uses only positive and unlabeled data for binary classification problems, to obtain the appropriate metric for discriminating foreground and background regions on each unlabeled image. Our PU learning makes us easy to select pseudo-labels for various background regions. The experimental results show the effectiveness of our method.
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