Abstract
U-net deep neural network has shown good performances in medical image segmentation analysis. Most of the existing works are a single use of upsampling algorithm or deconvolution algorithm in the expansion path, but they are not opposites. In this paper, we proposed a U-net network optimization strategy, in order to use the available annotation samples more effectively. One deconvolution layer and upsampling output layer were added in the splicing process of the high-resolution features in the contraction path, and then the obtained “feature map” was combined with the high-resolution features in the contraction path in the way that broaden the channel. The training data used in the experiment is the pathological section image of prostate tumor. The average Dice scores for models based on our optimization strategy improve from 0.749 to 0.813. It proves that the deconvolution algorithm can extract feature information different from the upsampling algorithm, and the complementarity can achieve a better data enhancement effect.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (61703302) and partially supported by Shenzhen Science and Technology Foundation (JCYJ20170816093943197).
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Li, S., Xu, J., Chen, R. (2020). U-Net Neural Network Optimization Method Based on Deconvolution Algorithm. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_50
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