Abstract
A method based on convolutional neural network auto encoder-decoder for low dose lung CT image noise reduction is presented. This method integrated convolutional neural network, auto encoder-decoder, residual learning, parametric rectified linear unit (PReLU). Particularly, the term of structural similarity index (SSIM) added to the loss function. After training patch by patch, the model attains a promising performance compared to state of the art traditional and deep learning methods in visual effects and quantitative measurements.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (Grant 61401049).
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Ma, Y., Feng, P., He, P., Long, Z., Wei, B. (2020). A Deep Learning Based Method for Low Dose Lung CT Noise Reduction. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_68
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DOI: https://doi.org/10.1007/978-981-32-9682-4_68
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