Sub-region aware retrieval-based network with multimodal prior knowledge guidance for microvascular invasion prediction in PET/CT imaging-Reference-Cited by-同舟云学术

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Sub-region aware retrieval-based network with multimodal prior knowledge guidance for microvascular invasion prediction in PET/CT imaging

Published:2024-04-17 Issue:19 Volume:36 Page:11623-11641
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Wu Nan,Wang Yutao,Zhang Jian,Yu Zhongfei,Jin Wei

Funder

National Natural Science Foundation of China

Joint Funds of the Zhejiang Provincial Natural Science Foundation of China

Public Welfare Science and Technology Project of Ningbo

Youth Key Health Talents Project of Ningbo

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00521-024-09777-w.pdf

Reference50 articles.

1. Appadurai JP, Kavin BP, Lai WC (2023) En-denet based segmentation and gradational modular network classification for liver cancer diagnosis. Biomedicines 11(5):1309

2. Banerjee S, Wang DS, Kim HJ et al (2015) A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology 62(3):792–800

3. Cao L, Wang Q, Hong J et al (2023) Mvi-tr: A transformer-based deep learning model with contrast-enhanced ct for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Cancers 15(5):1538

4. Choe J, Hwang HJ, Seo JB et al (2022) Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest ct. Radiology 302(1):187–197

5. Craig AJ, Von Felden J, Garcia-Lezana T et al (2020) Tumor evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 17(3):139–152

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