In this study, our goal is to show the impact of self-supervised pre-training
of transformers for organ at risk (OAR) and tumor segmentation as compared to
costly fully-supervised learning. The proposed algorithm is called Monte Carlo
Transformer based U-Net (MC-Swin-U). Unlike many other available models, our
approach presents uncertainty quantification with Monte Carlo dropout strategy
while generating its voxel-wise prediction. We test and validate the proposed
model on both public and one private datasets and evaluate the gross tumor
volume (GTV) as well as nearby risky organs' boundaries. We show that
self-supervised pre-training approach improves the segmentation scores
significantly while providing additional benefits for avoiding large-scale
annotation costs.

本研究旨在展示自我监督预训练模型在器官风险和肿瘤分割任务中相对于昂贵的全监督学习的影响，所提出的 MC-Swin-U 算法采用了蒙特卡罗变换器，与许多其他模型不同，其使用蒙特卡罗 Dropout 戳降低了不确定性，并在公共和私人数据集上进行了测试和验证， 相比大规模注释成本提供了附加收益，并且明显提高了分割评分。