Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation. (arXiv:2207.11512v4 [cs.CV] UPDATED)

Abdominal organ segmentation has many important clinical applications, such
as organ quantification, surgical planning, and disease diagnosis. However,
manually annotating organs from CT scans is time-consuming and labor-intensive.
Semi-supervised learning has shown the potential to alleviate this challenge by
learning from a large set of unlabeled images and limited labeled samples. In
this work, we follow the self-training strategy and employ a high-performance
hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the
teacher model to generate precise pseudo …

architecture arxiv hybrid segmentation self-training semi-supervised training