Machine learning models for medical images can help physicians diagnose and
manage diseases. However, due to the fact that medical image annotation
requires a great deal of manpower and expertise, as well as the fact that
clinical departments perform image annotation based on task orientation, there
is the problem of having fewer medical image annotation data with more
unlabeled data and having many datasets that annotate only a single organ. In
this paper, we present UniMOS, the first universal framework for achieving the
utilization of fully and partially labeled images as well as unlabeled images.
Specifically, we construct a Multi-Organ Segmentation (MOS) module over
fully/partially labeled data as the basenet and designed a new target adaptive
loss. Furthermore, we incorporate a semi-supervised training module that
combines consistent regularization and pseudolabeling techniques on unlabeled
data, which significantly improves the segmentation of unlabeled data.
Experiments show that the framework exhibits excellent performance in several
medical image segmentation tasks compared to other advanced methods, and also
significantly improves data utilization and reduces annotation cost. Code and
models are available at: this https URL

UniMOS 是首个全面利用完全标注图像、部分标注图像和未标注图像的通用框架，其中包括多器官分割模块、新的目标自适应损失和用于未标注数据的半监督训练模块。实验证明，与其他先进方法相比，该框架在多个医学图像分割任务中表现出色，同时显著提高了数据利用率并降低了标注成本。