Intelligent diagnosis of bladder cancer with limited MRI data-Reference-Cited by-同舟云学术

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Intelligent diagnosis of bladder cancer with limited MRI data

Published:2022-06-21 Issue: Volume: Page:
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Chen Xiuqing,Xu Qianqian,Xu Huachang,Wu Jiawei,Zhu Enze,Zhu Hong^ORCID

Funder

Project for Research on Medical in Jiangsu Commission of Health

National Natural Science Foundation of China

The Youth Medical Science and Technology Innovation Project of Xuzhou Municipal Health Commission

Special support for Young Talents of the Affiliated Hospital of Xuzhou Medical University

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s12652-022-04026-1.pdf

Reference22 articles.

1. Aerts H, Velazquez ER, Leijenaar R et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:1–8. https://doi.org/10.1038/ncomms5644

2. AJCC Cancer Staging System (2020) http://www.cancerstaging.org/references-tools/Pages/What-is-Cancer-Staging.aspx. Accessed 28 June 2020

3. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216. https://doi.org/10.1109/TMI.2016.2535865

4. Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962–969. https://doi.org/10.1016/j.ophtha.2017.02.008

5. He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

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