Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation

• URL: http://arxiv.org/abs/2403.12767v1
• Date: Tue, 19 Mar 2024 14:32:21 GMT
• Title: Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
• Authors: Qiangguo Jin, Hui Cui, Changming Sun, Yang Song, Jiangbin Zheng, Leilei Cao, Leyi Wei, Ran Su,
• Abstract summary: hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. We propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture. We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model.
• Score: 21.973620376753594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture. We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model. The two-stage structure complements with the uncertainty regularization strategy to avoid introducing extra modules in solving uncertainties and the aggregation mechanisms enable multi-scale and multi-stage feature integration. Comprehensive experimental results over the MoNuSeg and CRAG datasets show that our PG-FANet outperforms other state-of-the-art methods and our semi-supervised learning framework yields competitive performance with a limited amount of labeled data.

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