Abstract
In this paper, we combine the advantages of convolution local correlation and translation invariance in CNN with Transformer’s ability to effectively capture long-term dependencies between pixels to produce high-quality pseudo labels. In order to segment images efficiently and quickly, we select nnU-Net [2] as the final segmentation network and use pseudo labels, unlabeled data and labeled data together to train the network, and then we use Generic U-Net [2], the backbone network of nnU-Net, as final prediction network. The mean DSC of the prediction results of our method on validation set of FLARE2022 Challenge [3] is 0.7580.
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Xin, R., Wang, L. (2022). Abdominal Multi-organ Segmentation Using CNN and Transformer. 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_24
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DOI: https://doi.org/10.1007/978-3-031-23911-3_24
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