Zobrazeno 1 - 10
of 634
pro vyhledávání: '"Hu Hongjie"'
Autor:
Wang, Hongyi, Luo, Luyang, Wang, Fang, Tong, Ruofeng, Chen, Yen-Wei, Hu, Hongjie, Lin, Lanfen, Chen, Hao
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally
Externí odkaz:
http://arxiv.org/abs/2312.01099
Autor:
Wang, Hongyi, Luo, Luyang, Wang, Fang, Tong, Ruofeng, Chen, Yen-Wei, Hu, Hongjie, Lin, Lanfen, Chen, Hao
Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually regarded as a Multiple Instance Learning (MIL) problem when only slide-
Externí odkaz:
http://arxiv.org/abs/2303.15749
Autor:
Wang, Hongyi, Lin, Lanfen, Hu, Hongjie, Chen, Qingqing, Li, Yinhao, Iwamoto, Yutaro, Han, Xian-Hua, Chen, Yen-Wei, Tong, Ruofeng
High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3D images remains a challenge due to their high spatial resolution and
Externí odkaz:
http://arxiv.org/abs/2210.14645
Autor:
Wu, Nengqiu a, b, Peng, Bo a, b, ⁎, Juhasz, Albert c, Hu, Hongjie a, b, Wu, Sicheng a, b, Yang, Xia a, b, Dai, Yanan a, b, Wang, Xin a, b
Publikováno v:
In Science of the Total Environment 1 December 2024 954
Autor:
Lian, Shuaitao a, Su, Jie c, Fatima, Israr c, Zhang, Yuan a, Kuang, Tiantian a, Hu, Hongjie a, Qu, Dongshuai a, Si, Hongbin a, ⁎, Sun, Wenjing b, ⁎
Publikováno v:
In International Journal of Biological Macromolecules October 2024 278 Part 2
Autor:
Zhang, Yue, Peng, Chengtao, Peng, Liying, Huang, Huimin, Tong, Ruofeng, Lin, Lanfen, Li, Jingsong, Chen, Yen-Wei, Chen, Qingqing, Hu, Hongjie, Peng, Zhiyi
Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or chann
Externí odkaz:
http://arxiv.org/abs/2108.00911
Autor:
Xu, Yingying, Cai, Ming, Lin, Lanfen, Zhang, Yue, Hu, Hongjie, Peng, Zhiyi, Zhang, Qiaowei, Chen, Qingqing, Mao, Xiongwei, Iwamoto, Yutaro, Han, Xian-Hua, Chen, Yen-Wei, Tong, Ruofeng
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase
Externí odkaz:
http://arxiv.org/abs/2103.00274
Autor:
Wei, Peiying a, b, 1, Hu, Qiuhui b, 1, He, Chengbin b, 1, Hua, Peng c, Yang, Di d, Shao, Chang e, Xie, Lesi e, Han, Zhijiang a, Zhou, Xiaoxuan b, ⁎⁎, Ding, Zhongxiang a, ⁎⁎⁎, Hu, Hongjie b, ⁎
Publikováno v:
In Heliyon 30 March 2024 10(6)
Autor:
Hu, Hongjie a, 1, Sun, Wenjing b, 1, Zhang, Lifang a, Zhang, Yuan a, Kuang, Tiantian a, Qu, Dongshuai a, Lian, Shuaitao a, Hu, Shanshan a, Cheng, Ming a, Xu, Yanping a, Liu, Song a, Qian, Yajing a, Lu, Yujie a, He, Lingzhi a, Cheng, Yumeng a, Si, Hongbin a, ⁎
Publikováno v:
In International Journal of Biological Macromolecules March 2024 261 Part 1
Autor:
Huang, Huimin, Lin, Lanfen, Tong, Ruofeng, Hu, Hongjie, Zhang, Qiaowei, Iwamoto, Yutaro, Han, Xianhua, Chen, Yen-Wei, Wu, Jian
Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation. Combining multi-scale features i
Externí odkaz:
http://arxiv.org/abs/2004.08790