Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
<|reference_start|>Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation: 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.<|reference_end|>
@article{jin2024inter-,
    title={Inter- and intra-uncertainty based feature aggregation model for
  semi-supervised histopathology image segmentation},
    author={Qiangguo Jin, Hui Cui, Changming Sun, Yang Song, Jiangbin Zheng,
  Leilei Cao, Leyi Wei, Ran Su},
    journal={Expert Systems with Applications, 2024, 238: 122093},
    year={2024},
    doi={10.1016/j.eswa.2023.122093},
    archivePrefix={arXiv},
    eprint={2403.12767},
    primaryClass={cs.CV}
}