Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Shen, Chuyun"'
Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usua
Externí odkaz:
http://arxiv.org/abs/2411.01904
The escalating global cancer burden underscores the critical need for precise diagnostic tools in oncology. This research employs deep learning to enhance lesion segmentation in PET/CT imaging, utilizing a dataset of 900 whole-body FDG-PET/CT and 600
Externí odkaz:
http://arxiv.org/abs/2409.09784
This study explores a workflow for automated segmentation of lesions in FDG and PSMA PET/CT images. Due to the substantial differences in image characteristics between FDG and PSMA, specialized preprocessing steps are required. Utilizing YOLOv8 for d
Externí odkaz:
http://arxiv.org/abs/2409.09766
Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the g
Externí odkaz:
http://arxiv.org/abs/2408.02635
Autor:
Shen, Chuyun, Li, Wenhao, Chen, Haoqing, Wang, Xiaoling, Zhu, Fengping, Li, Yuxin, Wang, Xiangfeng, Jin, Bo
Radiologists must utilize multiple modal images for tumor segmentation and diagnosis due to the limitations of medical imaging and the diversity of tumor signals. This leads to the development of multimodal learning in segmentation. However, the redu
Externí odkaz:
http://arxiv.org/abs/2401.02717
Autor:
Sheng, Junjie, Huang, Zixiao, Shen, Chuyun, Li, Wenhao, Hua, Yun, Jin, Bo, Zha, Hongyuan, Wang, Xiangfeng
The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks? To investigate this, we f
Externí odkaz:
http://arxiv.org/abs/2312.03290
The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high annotation cost, the medical image segmentation (MIS) community has b
Externí odkaz:
http://arxiv.org/abs/2306.08958
Autor:
Li, Wenhao, Xu, Qisen, Shen, Chuyun, Hu, Bin, Zhu, Fengping, Li, Yuxin, Jin, Bo, Wang, Xiangfeng
Medical image segmentation is one of the fundamental problems for artificial intelligence-based clinical decision systems. Current automatic medical image segmentation methods are often failed to meet clinical requirements. As such, a series of inter
Externí odkaz:
http://arxiv.org/abs/2111.07716
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.