Advancing healthcare with LDCT image denoising through self-regularization and UDA
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03065-9.pdf
Reference28 articles.
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2. Liu, J., Kang, Y., Liu, T., Zhang, T., Zhang, Y.: Deep iterative reconstruction network based on residual constraint for low-dose CT imaging. In: 2022 8th International Conference on Systems and Informatics (ICSAI), pp. 1–6. IEEE (2022)
3. Jing, J., Xia, W., Hou, M., Chen, H., Liu, Y., Zhou, J., Zhang, Y.: Training low dose CT denoising network without high quality reference data. Phys. Med. Biol. 67(8), 084002 (2022)
4. Tsai, H.X., Kang, L.W.: Cross domain deep learning for noise removal from LDCT images. In: 2022 IEEE International Conference on Consumer Electronics-Taiwan, pp. 475–476. IEEE (2022)
5. Rawashdeh, M.A., Saade, C.: Radiation dose reduction considerations and imaging patterns of ground glass opacities in coronavirus: risk of over exposure in computed tomography. Radiol. Med. (Torino) 126, 380–387 (2021)
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