Abstract
In this paper, we adopt a “pseudo-labeling” approach to semi-supervised learning based on 50 labeled images and 2000 unlabeled images. This approach yields a model with 0.7496 mean DSC on the validation set, outperforming the 0.6903 mean DSC of the model with only 50 labeled images.
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Acknowledgements
The authors of this paper declare that the segmentation method they implemented for participation in the FLARE 2022 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. The proposed solution is fully automatic without any manual intervention.
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Gao, J., Xu, J., Fei, H. (2022). A Pseudo-labeling Approach to Semi-supervised Organ Segmentation. In: Ma, J., Wang, B. (eds) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. FLARE 2022. Lecture Notes in Computer Science, vol 13816. Springer, Cham. https://doi.org/10.1007/978-3-031-23911-3_28
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DOI: https://doi.org/10.1007/978-3-031-23911-3_28
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