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pro vyhledávání: '"Bian Yun"'
Autor:
Qiu, Zhongwei, Chao, Hanqing, Liu, Wenbin, Shen, Yixuan, Lu, Le, Yan, Ke, Jin, Dakai, Bian, Yun, Jiang, Hui
Survival analysis using pathology images poses a considerable challenge, as it requires the localization of relevant information from the multitude of tiles within whole slide images (WSIs). Current methods typically resort to a two-stage approach, w
Externí odkaz:
http://arxiv.org/abs/2409.03804
Publikováno v:
Zhongguo linchuang yanjiu, Vol 37, Iss 7, Pp 1055-1059 (2024)
"Methods A retrospective study was conducted to select 98 patients with DN who were treated in Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine from January 2020 to September 2022. Follow-up was conducted after treatment, with
Externí odkaz:
https://doaj.org/article/ab68dd87295b49bdad60db024587b68f
Publikováno v:
Chinese Journal of Magnetic Resonance, Vol 41, Iss 2, Pp 151-161 (2024)
The pancreas has always been one of the most challenging parts in medical image segmentation due to its complex anatomical structure and complex surrounding environment. Aiming at the above problems, a deep learning segmentation model combining dual
Externí odkaz:
https://doaj.org/article/e794eedfdfe241f093d879920ff7f02a
Autor:
Zheng, Zhilin, Fang, Xu, Yao, Jiawen, Zhu, Mengmeng, Lu, Le, Huang, Lingyun, Xiao, Jing, Shi, Yu, Lu, Hong, Lu, Jianping, Zhang, Ling, Shao, Chengwei, Bian, Yun
Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors. Preoperative prediction
Externí odkaz:
http://arxiv.org/abs/2301.01448
Publikováno v:
Chinese Journal of Magnetic Resonance, Vol 41, Iss 1, Pp 19-29 (2024)
This study aims to classify and differentiate mucinous and serous cystic neoplasms of the pancreas using a multi-source feature classification model based on deep learning for preoperative auxiliary diagnosis. Deep learning features and radiomics fea
Externí odkaz:
https://doaj.org/article/0f39515cb3b044709275e788cc131751
Publikováno v:
Chinese Journal of Magnetic Resonance, Vol 40, Iss 03, Pp 270-279 (2023)
This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor (MCN) from serous cystic tumor (SCN) of the pancreas. Firstly, the data of 65 MCNs and 107 SCNs from Changhai Hospital were augem
Externí odkaz:
https://doaj.org/article/2efbddadeb3a4d53b50543436138da36
Publikováno v:
In Bioorganic Chemistry May 2024 146
Autor:
Zhang, Gaofeng, Zhan, Qian, Gao, Qingyu, Mao, Kuanzheng, Yang, Panpan, Gao, Yisha, Wang, Lijia, Song, Bin, Chen, Yufei, Bian, Yun, Shao, Chengwei, Lu, Jianping, Ma, Chao
Publikováno v:
In Computers in Biology and Medicine March 2024 170
Accurate and automated tumor segmentation is highly desired since it has the great potential to increase the efficiency and reproducibility of computing more complete tumor measurements and imaging biomarkers, comparing to (often partial) human measu
Externí odkaz:
http://arxiv.org/abs/2008.10652
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