  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: https://github.com/lw8807001/UniMOS.
