Abstract
Acquiring pixel-level annotations for histological image segmentation is time- and labor- consuming. Semi-supervised learning enables learning from the unlabeled and limited amount of labeled data. A challenging issue is the inconsistent and uncertain predictions on unlabeled data. To enforce invariant predictions over the perturbations applied to the hidden feature space, we propose a Mean-Teacher based hierarchical consistency enforcement (HCE) framework and a novel hierarchical consistency loss (HC-loss) with learnable and self-guided mechanisms. Specifically, the HCE takes the perturbed versions of the hierarchical features from the encoder as input to the auxiliary decoders, and encourages the predictions of the auxiliary decoders and the main decoder to be consistent. The HC-loss facilitates the teacher model to generate reliable guidance and enhances the consistency among all the decoders of the student model. The proposed method is simple, yet effective, which can easily be extended to other frameworks. The quantitative and qualitative experimental results indicate the effectiveness of the hierarchical consistency enforcement on the MoNuSeg and CRAG datasets.
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Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China [Grant No. 62072329], and the National Key Technology R &D Program of China [Grant No. 2018YFB1701700].
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Jin, Q. et al. (2022). Semi-supervised Histological Image Segmentation via Hierarchical Consistency Enforcement. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_1
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