MeFD-Net: multi-expert fusion diagnostic network for generating radiology image reports-Reference-Cited by-同舟云学术

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MeFD-Net: multi-expert fusion diagnostic network for generating radiology image reports

Published:2024-08-24 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Ran Ruisheng,Pan Renjie,Yang Wen,Deng Yan,Zhang Wenfeng^ORCID,Hu Wei,Qing Qibing

Funder

Natural Science Foundation of Chongqing

Science and Technology Research Program of Chongqing Municipal Education Commission

Key Project for Science and Technology Research Program of Chongqing Municipal Education Commission

Shanghai Municipal Natural Science Foundation

Chongqing Normal University Foundation

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05680-y.pdf

Reference37 articles.

1. Rettenberger L, Schilling M, Elser S, Böhland M, Reischl M (2023) Self-supervised learning for annotation efficient biomedical image segmentation. IEEE Trans Biomed Eng 70(9):2519–2528

2. Deng R, Liu Q, Cui C, Yao T, Long J, Asad Z, Womick RM, Zhu Z, Fogo AB, Zhao S, Yang H, Huo Y (2023) Omni-seg: A scale-aware dynamic network for renal pathological image segmentation. IEEE Trans Biomed Eng 70(9):2636–2644

3. Xia S, Zhu H, Liu X, Gong M, Huang X, Xu L, Zhang H, Guo J (2020) Vessel segmentation of x-ray coronary angiographic image sequence. IEEE Trans Biomed Eng 67(5):1338–1348

4. Guan H, Liu M (2022) Domain adaptation for medical image analysis: A survey. IEEE Trans Biomed Eng 69(3):1173–1185

5. Li M, Liu R, Wang F, Chang X, Liang X (2023) Auxiliary signal-guided knowledge encoder-decoder for medical report generation. World Wide Web 26(1):253–270

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