WAE-TLDN: self-supervised fusion for multimodal medical images via a weighted autoencoder and a tensor low-rank decomposition network-Reference-Cited by-同舟云学术

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WAE-TLDN: self-supervised fusion for multimodal medical images via a weighted autoencoder and a tensor low-rank decomposition network

Published:2024-01 Issue:2 Volume:54 Page:1656-1671
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Pan Linna,Nie Rencan^ORCID,Zhang Gucheng,Cao Jinde,Han Yao

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Program of Yunnan Key Laboratory of Intelligent Systems and Computing

Postgraduate Science Foundation of Yunnan University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-05097-z.pdf

Reference57 articles.

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2. Wu HT, Zheng K, Huang Q, Hu J (2021) Contrast enhancement of multiple tissues in mr brain images with reversibility. IEEE Signal Process Lett 28:160–164. https://doi.org/10.1109/LSP.2020.3048840

3. Valladares A, Beyer T, Rausch I (2020) Physical imaging phantoms for simulation of tumor heterogeneity in pet, ct, and mri: an overview of existing designs. Med Phys 47(4):2023–2037

4. Zhang M, Chu C, Huang L, Hu B (2022) Ct-mr image fusion for post-implant dosimetry analysis in brain tumor seed implantation-a preliminary study. Dis Markers 2022

5. Liu X, Li W, Liu Z, Du F, Zou Q (2021) A dual-branch model for diagnosis of parkin-son’s disease based on the independent and joint features of the left and right gait. Appl Intell 1–12

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