Self-Adaptive Teacher-Student framework for colon polyp segmentation from unannotated private data with public annotated datasets
Yiwen Jia, Guangming Feng, Tang Yang, Siyuan Chen, Fu Dai, Kazunori Nagasaka, Kazunori Nagasaka, Kazunori Nagasaka, Kazunori Nagasaka
TL;DR
This paper introduces a new framework for colon polyp segmentation using unannotated private data and public datasets, improving performance through a teacher-student model and novel strategies.
Contribution
The novel SATS framework with UDFusion and GANet improves colon polyp segmentation in unannotated private data using public datasets.
Findings
SATS achieved 76.30% IoU, 86.00% Recall, and 7.01 pixels HD on colon polyp segmentation.
The framework outperformed five existing methods on both public and private datasets.
UDFusion and GANet effectively bridge the gap between public and private data distributions.
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 teacher networks, the SATS includes a newly proposed Uncertainty and Distance Fusion (UDFusion)…
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Taxonomy
TopicsColorectal Cancer Screening and Detection · Radiomics and Machine Learning in Medical Imaging · COVID-19 diagnosis using AI