Abstract
Pre-training can alleviate the requirement of labeling data for a new task. However, Pre-training as a sequential learning typically suffers in fact from forgetting the older tasks. Especially in complex medical image segmentation tasks, this problem is more prominent. To solve above problem, we propose a network structure based on feature space transformation (FS-Net) for data expansion of medical image segmentation. FS-Net share parameters during training to help exploiting regularities present across tasks and improving the performance by constraining the learned representation. In the experiment, we use M&Ms as the extended dataset of HVSMR, these two tasks have the same segmentation target (heart). The segmentation accuracy of FS-Net is up to 7.12% higher than the baseline network, which is significantly better than Pre-training. In addition, we use Brats2019 as expansion dataset on WMH, and the segmentation accuracy is improved by 0.77% compared with the baseline network. And Brats2019 (glioma) and WMH (white matter hyperintensities) have different segmentation targets.
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Acknowledgment
This study is supported by grants from the National Key Research and Development Program of China (2018YFC1312000) and Basic Research Foundation of Shenzhen Science and Technology Stable Support Program (GXWD20201230155 427003-20200822115709001).
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Guo, X., Yang, Y., Ma, T. (2021). FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation. In: Engelhardt, S., et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_21
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DOI: https://doi.org/10.1007/978-3-030-88210-5_21
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