Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
Autor: | Guodong Wei, Lingjie Liu, Runnan Chen, Nenglun Chen, Zhiming Cui, Wenping Wang, Yuexin Ma |
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Rok vydání: | 2021 |
Předmět: |
Structure (mathematical logic)
FOS: Computer and information sciences Landmark Computer science Property (programming) business.industry Cognitive Neuroscience Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Pattern recognition Shape constraint Computer Science Applications Image (mathematics) Anatomical landmark ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Artificial intelligence Focus (optics) business Self training |
DOI: | 10.48550/arxiv.2105.13593 |
Popis: | Well-annotated medical images are costly and sometimes even impossible to acquire, hindering landmark detection accuracy to some extent. Semi-supervised learning alleviates the reliance on large-scale annotated data by exploiting the unlabeled data to understand the population structure of anatomical landmarks. The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods. In this paper, we propose a model-agnostic shape-regulated self-training framework for semi-supervised landmark detection by fully considering the global shape constraint. Specifically, to ensure pseudo labels are reliable and consistent, a PCA-based shape model adjusts pseudo labels and eliminate abnormal ones. A novel Region Attention loss to make the network automatically focus on the structure consistent regions around pseudo labels. Extensive experiments show that our approach outperforms other semi-supervised methods and achieves notable improvement on three medical image datasets. Moreover, our framework is flexible and can be used as a plug-and-play module integrated into most supervised methods to improve performance further. Comment: Accepted to Neurocomputing |
Databáze: | OpenAIRE |
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