Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Hailiang Ye"'
Publikováno v:
Zhongguo aizheng zazhi, Vol 33, Iss 3, Pp 191-200 (2023)
Renal cell carcinoma (RCC) is one of the three major urinary system tumors. With the changes of lifestyle and the rise of obesity, hypertension and other diseases, the incidence of RCC is increasing. The onset of RCC is hidden, and RCC has strong het
Externí odkaz:
https://doaj.org/article/9225b53dc6634e75b998670590f15493
Publikováno v:
Zhongguo aizheng zazhi, Vol 32, Iss 4, Pp 287-297 (2022)
The incidence of renal malignancies is increasing each year. Clear cell renal cell carcinoma (ccRCC) accounts for approximately 80% of all renal malignancies. Its unique genetic background and mutation features involve dysregulation of homeostasis wi
Externí odkaz:
https://doaj.org/article/8299d3e16a5a424da86ca6abffa1aaf4
Autor:
XU Wenhao, TIAN Xi, Aihetaimujiang·Anwaier , QU Yuanyuan, SHI Guohai, ZHANG Hailiang, YE Dingwei
Publikováno v:
Zhongguo aizheng zazhi, Vol 32, Iss 1, Pp 68-74 (2022)
Recently, advances in machine learning and neural network technology have allowed artificial intelligence (AI) to further promote guidance of clinical diagnosis, treatment and resource expenditures. In genitourinary cancers, AI has made huge progress
Externí odkaz:
https://doaj.org/article/2a26bcf45ecd4a989633cd7f6d10650e
Publikováno v:
Neural Networks. 157:444-459
Graph neural networks (GNNs) have shown strong graph-structured data processing capabilities. However, most of them are generated based on the message-passing mechanism and lack of the systematic approach to guide their developments. Meanwhile, a uni
Publikováno v:
International Journal of Machine Learning and Cybernetics.
Publikováno v:
International Journal of Wavelets, Multiresolution and Information Processing.
Publikováno v:
Pattern Recognition. 142:109687
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-13
The use of deep learning methods in hyperspectral image (HSI) classification has been a promising approach due to its powerful ability to automatically extract features in recent years. This article proposes a novel deep framework for HSI classificat
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-12
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-15
Although convolutional neural networks (CNNs) have shown good performance on grid data, they are limited in the semantic segmentation of irregular point clouds. This article proposes a novel and effective graph CNN framework, referred to as the local