Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Zhu, Xinliang"'
Multimodal search has become increasingly important in providing users with a natural and effective way to ex-press their search intentions. Images offer fine-grained details of the desired products, while text allows for easily incorporating search
Externí odkaz:
http://arxiv.org/abs/2404.15790
Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics
Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patches because, for most occasions, o
Externí odkaz:
http://arxiv.org/abs/2211.16632
Autor:
Wang, Xueqi, Mao, Ying, Liang, Tingyu, Li, Zhengdou, Li, Xu, Zhu, Xinliang, Cao, Fuliang, Zhang, Ji
Publikováno v:
In Food Bioscience June 2024 59
Autor:
Zhu, Xinliang, Zhang, Shunbin, Liu, Xiaoxiao, Li, Huixia, Zhu, Xinyu, Zhang, Ji, Wang, Xiaopeng, Zhang, Min
Publikováno v:
In Ecotoxicology and Environmental Safety 1 January 2024 269
Proxy-based metric learning losses are superior to pair-based losses due to their fast convergence and low training complexity. However, existing proxy-based losses focus on learning class-discriminative features while overlooking the commonalities s
Externí odkaz:
http://arxiv.org/abs/2103.13538
Publikováno v:
Shipin Kexue, Vol 44, Iss 9, Pp 47-57 (2023)
The effect of microwave pretreatment of sesame seeds on the flavor, sensory quality, nutrition, and safety of sesame oil was systematically studied to provide a theoretical foundation for the application of microwave for aroma enhancement in sesame o
Externí odkaz:
https://doaj.org/article/9c6cca22080b4ef89b94accad9f58b6c
Publikováno v:
Medical Image Analysis, Volume 65, October 2020, 101789
Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathol
Externí odkaz:
http://arxiv.org/abs/2009.11169
Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation. One of the most common strategies is to translate images from the source domain to the target domain and then align their marginal di
Externí odkaz:
http://arxiv.org/abs/2003.04614
Autor:
Chen, Yongkang, Wang, Ziqiao, Liu, Jieping, Guo, Yucai, Chen, Anqi, Chen, Baoyang, Hu, Peng, Zhu, Xinliang, Li, Wenjie, Zhao, Wei, Niu, Jin
Publikováno v:
In LWT 15 August 2023 186
Autor:
Wang, Xueqi, Liang, Tingyu, Mao, Ying, Li, Zhengdou, Li, Xu, Zhu, Xinliang, Cao, Fuliang, Zhang, Ji
Publikováno v:
In Phytomedicine August 2023 117