Zobrazeno 1 - 10
of 310
pro vyhledávání: '"Jiang Haochuan"'
Brain tumor segmentation is often based on multiple magnetic resonance imaging (MRI). However, in clinical practice, certain modalities of MRI may be missing, which presents an even more difficult scenario. To cope with this challenge, knowledge dist
Externí odkaz:
http://arxiv.org/abs/2409.19366
Brain tumor segmentation is often based on multiple magnetic resonance imaging (MRI). However, in clinical practice, certain modalities of MRI may be missing, which presents a more difficult scenario. To cope with this challenge, Knowledge Distillati
Externí odkaz:
http://arxiv.org/abs/2408.09465
Publikováno v:
2018, Neural Information Processing - 25th International Conference, ICONIP
Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an ef
Externí odkaz:
http://arxiv.org/abs/2406.06122
Publikováno v:
International Conference on Brain Inspired Cognitive Systems 2023
Synthesizing Chinese characters with consistent style using few stylized examples is challenging. Existing models struggle to generate arbitrary style characters with limited examples. In this paper, we propose the Generalized W-Net, a novel class of
Externí odkaz:
http://arxiv.org/abs/2406.06847
Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information. Despite recent efforts using CNNs and ViTs to predict annotations of different scales,
Externí odkaz:
http://arxiv.org/abs/2403.19177
Thanks to the capacity for long-range dependencies and robustness to irregular shapes, vision transformers and deformable convolutions are emerging as powerful vision techniques of segmentation.Meanwhile, Graph Convolution Networks (GCN) optimize loc
Externí odkaz:
http://arxiv.org/abs/2210.05151
Autor:
Li, Lei, Wu, Fuping, Wang, Sihan, Luo, Xinzhe, Martin-Isla, Carlos, Zhai, Shuwei, Zhang, Jianpeng, Liu7, Yanfei, Zhang, Zhen, Ankenbrand, Markus J., Jiang, Haochuan, Zhang, Xiaoran, Wang, Linhong, Arega, Tewodros Weldebirhan, Altunok, Elif, Zhao, Zhou, Li, Feiyan, Ma, Jun, Yang, Xiaoping, Puybareau, Elodie, Oksuz, Ilkay, Bricq, Stephanie, Li, Weisheng, Punithakumar, Kumaradevan, Tsaftaris, Sotirios A., Schreiber, Laura M., Yang, Mingjing, Liu, Guocai, Xia, Yong, Wang, Guotai, Escalera, Sergio, Zhuang, Xiahai
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medic
Externí odkaz:
http://arxiv.org/abs/2201.03186
Publikováno v:
MICCAI-2020 MyoPS Challenge Paper
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the performance of relevant algorithms is significantly affected by the pr
Externí odkaz:
http://arxiv.org/abs/2009.02569
Autor:
Jiang, Haochuan, Chartsias, Agisilaos, Zhang, Xinheng, Papanastasiou, Giorgos, Semple, Scott, Dweck, Mark, Semple, David, Dharmakumar, Rohan, Tsaftaris, Sotirios A.
Publikováno v:
MICCAI-2020 DART workshop
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An
Externí odkaz:
http://arxiv.org/abs/2009.02564
Autor:
Luo, Zhaohua, Zhuang, Yijun, Li, Wen, Du, Yongxiao, Sun, Jinghan, Liu, Zehu, Wu, Yuntao, Jiang, Haochuan, Jiang, Jun
Publikováno v:
In Applied Materials Today December 2023 35