用无监督图像翻译生成组织切片伪标签，解决标注数据少的问题。
Unpaired Modality Translation for Pseudo Labeling of Histology Images
- 通过域间无监督翻译，将已标注图像转为未标注域的伪标签。
- 在SEM数据上达到0.736的平均Dice分数，优于传统方法。
- 适合需要快速获取初始标注的生物医学图像分割研究者。
组织学图像分割对多种生物医学应用至关重要，但标注数据匮乏构成重大挑战。本文提出一种显微镜伪标签流水线，利用无监督图像翻译解决该问题。方法通过在已标注与未标注域之间进行图像转换，无需目标域的先验标注即可生成伪标签。我们在三个与标注数据差异逐渐增大的图像域上评估了两种伪标签策略，结果表明其有效性。特别地，采用教学路径（tutoring path）在SEM数据集上取得了0.736±0.005的平均Dice分数，即用透射电镜（TEM）数据翻译生成合成的扫描电镜（SEM）数据训练分割模型。该方法旨在通过提供高质量伪标签，加速后续人工修正过程。
原文摘要 · Abstract (English)
The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to address this issue. Our method generates pseudo labels by translating between labeled and unlabeled domains without requiring prior annotation in the target domain. We evaluate two pseudo labeling strategies across three image domains increasingly dissimilar from the labeled data, demonstrating their effectiveness. Notably, our method achieves a mean Dice score of $0.736 \pm 0.005$ on a SEM dataset using the tutoring path, which involves training a segmentation model on synthetic data created by translating the labeled dataset (TEM) to the target modality (SEM). This approach aims to accelerate the annotation process by providing high-quality pseudo labels as a starting point for manual refinement.
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