LiTS17

该数据集是一份用于评估所提出MONA框架性能的医疗图像分割数据集。在三种不同的标注比例（1%、5%、10%）下进行了评估，结果显示，尤其是在标注数据有限的情况下，MONA的表现优于其他自监督学习方法。该数据集的任务是医疗图像分割。

This is a medical image segmentation dataset used to evaluate the performance of the proposed MONA framework. It was evaluated under three different annotation ratios (1%, 5%, and 10%), and the results showed that MONA outperforms other self-supervised learning methods, especially when labeled data is limited. The task of this dataset is medical image segmentation.