Self-Adaptive Teacher-Student framework for colon polyp segmentation from unannotated private data with public annotated datasets.

ABSTRACT: Colon polyps have become a focal point of research due to their heightened potential to develop into appendiceal cancer, which has the highest mortality rate globally. Although numerous colon polyp segmentation methods have been developed using public polyp datasets, they tend to underperform on private datasets due to inconsistencies in data distribution and the difficulty of fine-tuning without annotations. In this paper, we propose a Self-Adaptive Teacher-Student (SATS) framework to segment colon polyps from unannotated private data by utilizing multiple publicly annotated datasets. The SATS trains multiple teacher networks on public datasets and then generates pseudo-labels on private data to assist in training a student network. To enhance the reliability of the pseudo-labels from the

SUBMITTER: Jia Y 

PROVIDER: S-EPMC11356409 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature