Zobrazeno 1 - 10
of 317
pro vyhledávání: '"Jiang, Yuncheng"'
Autor:
Jiang, Yuncheng, Zhang, Zixun, Wei, Jun, Feng, Chun-Mei, Li, Guanbin, Wan, Xiang, Cui, Shuguang, Li, Zhen
AI-assisted lesion detection models play a crucial role in the early screening of cancer. However, previous image-based models ignore the inter-frame contextual information present in videos. On the other hand, video-based models capture the inter-fr
Externí odkaz:
http://arxiv.org/abs/2408.14051
Autor:
Jiang, Yuncheng, Hu, Yiwen, Zhang, Zixun, Wei, Jun, Feng, Chun-Mei, Tang, Xuemei, Wan, Xiang, Liu, Yong, Cui, Shuguang, Li, Zhen
Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the lack of a large-scale ERUS dataset with high-quality annotations hinders
Externí odkaz:
http://arxiv.org/abs/2408.10067
How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this paper, a landscape-aware differential evolution (LADE) algorithm is
Externí odkaz:
http://arxiv.org/abs/2408.02340
In this paper, we propose an Openspace Collision-freE trAjectory plaNner (OCEAN) for autonomous parking. OCEAN is an optimization-based trajectory planner accelerated by Alternating Direction Method of Multiplier (ADMM) with enhanced computational ef
Externí odkaz:
http://arxiv.org/abs/2403.05090
Autor:
Jiang, Yuncheng, Zhang, Zixun, Hu, Yiwen, Li, Guanbin, Wan, Xiang, Wu, Song, Cui, Shuguang, Huang, Silin, Li, Zhen
Accurate polyp detection is critical for early colorectal cancer diagnosis. Although remarkable progress has been achieved in recent years, the complex colon environment and concealed polyps with unclear boundaries still pose severe challenges in thi
Externí odkaz:
http://arxiv.org/abs/2401.04961
Automatic polyp segmentation models play a pivotal role in the clinical diagnosis of gastrointestinal diseases. In previous studies, most methods relied on fully supervised approaches, necessitating pixel-level annotations for model training. However
Externí odkaz:
http://arxiv.org/abs/2311.05122
Autor:
Du, Yuhao, Jiang, Yuncheng, Tan, Shuangyi, Wu, Xusheng, Dou, Qi, Li, Zhen, Li, Guanbin, Wan, Xiang
Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment. However, as medical image annotation is labour- and resource-intensive, the scarcity of annotated data limits
Externí odkaz:
http://arxiv.org/abs/2309.01111
Accurate polyp detection is essential for assisting clinical rectal cancer diagnoses. Colonoscopy videos contain richer information than still images, making them a valuable resource for deep learning methods. Great efforts have been made to conduct
Externí odkaz:
http://arxiv.org/abs/2306.03686
Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs). Among various attempts to improve SGD, weight averaging (WA), which averages the weights of multiple models, has recentl
Externí odkaz:
http://arxiv.org/abs/2304.11519
In 3D medical image segmentation, small targets segmentation is crucial for diagnosis but still faces challenges. In this paper, we propose the Axis Projection Attention UNet, named APAUNet, for 3D medical image segmentation, especially for small tar
Externí odkaz:
http://arxiv.org/abs/2210.01485