Zobrazeno 1 - 10
of 839
pro vyhledávání: '"DING Tong"'
Publikováno v:
Renal Failure, Vol 46, Iss 1 (2024)
Background Hemodialysis (HD) and peritoneal dialysis (PD) are effective ways to treat end-stage renal disease (ERSD). This study aimed to investigate the differences in survival and the factors that influence it in patients with end-stage renal disea
Externí odkaz:
https://doaj.org/article/c2338111ebd346dda467b2c12b68ccd6
Publikováno v:
BMC Chemistry, Vol 17, Iss 1, Pp 1-8 (2023)
Abstract The formation and emission of sulfur trioxide (SO3) in sulfur recovery unit has received increasing attention due to its adverse effects on the operation of plant and environment. Due to the excess oxygen, high concentration of SO2 and high
Externí odkaz:
https://doaj.org/article/3885f7f89f4345708efe5c84843acdee
Publikováno v:
口腔疾病防治, Vol 31, Iss 8, Pp 543-551 (2023)
Objective To investigate the effect of micro/nano hierarchical structures on the adhesion and proliferation of MC3T3-E1 cells, evaluate the drug delivery potential of titanium surfaces, and provide a reference for the modification of selected areas o
Externí odkaz:
https://doaj.org/article/32bb366ac3c74f56ab68a5938b64af36
Autor:
Ding, Tong, Wagner, Sophia J., Song, Andrew H., Chen, Richard J., Lu, Ming Y., Zhang, Andrew, Vaidya, Anurag J., Jaume, Guillaume, Shaban, Muhammad, Kim, Ahrong, Williamson, Drew F. K., Chen, Bowen, Almagro-Perez, Cristina, Doucet, Paul, Sahai, Sharifa, Chen, Chengkuan, Komura, Daisuke, Kawabe, Akihiro, Ishikawa, Shumpei, Gerber, Georg, Peng, Tingying, Le, Long Phi, Mahmood, Faisal
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). Howe
Externí odkaz:
http://arxiv.org/abs/2411.19666
Prompt learning has proven effective in adapting vision language models for downstream tasks. However, existing methods usually append learnable prompt tokens solely with the category names to obtain textual features, which fails to fully leverage th
Externí odkaz:
http://arxiv.org/abs/2410.11201
Publikováno v:
Energies, Vol 16, Iss 11, p 4286 (2023)
A low specific speed centrifugal compressor with leading/trailing edge combined sweep blades is proposed. The performance and internal flow field characteristics are analyzed in detail by numerical simulations, and a bench test is carried out. It is
Externí odkaz:
https://doaj.org/article/ae7b03c7b26b420aa0a1e037966f91b0
Publikováno v:
Energies, Vol 16, Iss 9, p 3869 (2023)
Although inlet bent pipes are usually adopted due to limited installation space, the influences of different bend pipes on the inlet flow characteristics and performance of centrifugal compressors are still unclear. The numerical simulation of a cent
Externí odkaz:
https://doaj.org/article/c9b469a37ec14e4c8a38b8ed82f4cc8f
Autor:
Song, Andrew H., Chen, Richard J., Ding, Tong, Williamson, Drew F. K., Jaume, Guillaume, Mahmood, Faisal
Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However, the slide representations resulting from this approach are highly tailored to specific cl
Externí odkaz:
http://arxiv.org/abs/2405.11643
In this paper, we propose a structure-guided Gauss-Newton (SgGN) method for solving least squares problems using a shallow ReLU neural network. The method effectively takes advantage of both the least squares structure and the neural network structur
Externí odkaz:
http://arxiv.org/abs/2404.05064
Autor:
Lu, Ming Y., Chen, Bowen, Williamson, Drew F. K., Chen, Richard J., Ikamura, Kenji, Gerber, Georg, Liang, Ivy, Le, Long Phi, Ding, Tong, Parwani, Anil V, Mahmood, Faisal
The field of computational pathology has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intell
Externí odkaz:
http://arxiv.org/abs/2312.07814