Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer-Reference-Cited by-同舟云学术

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Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer

Published:2023-10-01 Issue: Volume: Page:653-658
ISSN:
Container-title:2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Gao Xiangneng¹,Ruan Shulan²,Shi Jun²,Hu Guoqing³,Wei Wei³

Affiliation:

1. Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China,518055

2. School of Computer Science and Technology, University of Science and Technology of China,Hefei,China,230026

3. First Affiliated Hospital of USTC,Department of Radiology,Hefei,China,230001

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10391856/10393862/10394014.pdf?arnumber=10394014

Reference36 articles.

1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

2. A CT-based radiomics nomogram for predicting early recurrence in patients with high-grade serous ovarian cancer

3. Ultrasound evaluation of intra-abdominal sites of disease to predict likelihood of suboptimal cytoreduction in advanced ovarian cancer: a prospective study

4. Laparoscopy for diagnosing resectability of disease in women with advanced ovarian cancer

5. Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Decision-Level Fusion Classification of Ovarian CT Benign and Malignant Tumors Based on Radiomics and Deep Learning of Dual Views;IEEE Access;2024

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