Zobrazeno 1 - 10
of 2 476
pro vyhledávání: '"Wang Yaqi"'
In medical image analysis, achieving fast, efficient, and accurate segmentation is essential for automated diagnosis and treatment. Although recent advancements in deep learning have significantly improved segmentation accuracy, current models often
Externí odkaz:
http://arxiv.org/abs/2411.19447
Autor:
Wang, Yaqi, Xu, Haipei
Recently, as Large Language Models (LLMs) have shown impressive emerging capabilities and gained widespread popularity, research on LLM-based search agents has proliferated. In real-world situations, users often input contextual and highly personaliz
Externí odkaz:
http://arxiv.org/abs/2411.14574
We designed a Retrieval-Augmented Generation (RAG) system to provide large language models with relevant documents for answering domain-specific questions about Pittsburgh and Carnegie Mellon University (CMU). We extracted over 1,800 subpages using a
Externí odkaz:
http://arxiv.org/abs/2411.13691
Survival prediction for esophageal squamous cell cancer (ESCC) is crucial for doctors to assess a patient's condition and tailor treatment plans. The application and development of multi-modal deep learning in this field have attracted attention in r
Externí odkaz:
http://arxiv.org/abs/2408.13290
Autor:
Lu, Zhanyun, Gu, Renshu, Cheng, Huimin, Pang, Siyu, Xu, Mingyu, Xu, Peifang, Wang, Yaqi, Kinoshita, Yuichiro, Ye, Juan, Jia, Gangyong, Wu, Qing
Medical image datasets in the real world are often unlabeled and imbalanced, and Semi-Supervised Object Detection (SSOD) can utilize unlabeled data to improve an object detector. However, existing approaches predominantly assumed that the unlabeled d
Externí odkaz:
http://arxiv.org/abs/2408.12355
Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction an
Externí odkaz:
http://arxiv.org/abs/2407.15556
Autor:
Wang, Yaqi, Zhang, Yifan, Chen, Xiaodiao, Wang, Shuai, Qian, Dahong, Ye, Fan, Xu, Feng, Zhang, Hongyuan, Zhang, Qianni, Wu, Chengyu, Li, Yunxiang, Cui, Weiwei, Luo, Shan, Wang, Chengkai, Li, Tianhao, Liu, Yi, Feng, Xiang, Zhou, Huiyu, Liu, Dongyun, Wang, Qixuan, Lin, Zhouhao, Song, Wei, Li, Yuanlin, Wang, Bing, Wang, Chunshi, Chen, Qiupu, Li, Mingqian
Computer-aided design (CAD) tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation. In particular, the 2D panoramic X-ray image efficiently detects invisible caries, impacte
Externí odkaz:
http://arxiv.org/abs/2407.13246
Motor imagery electroencephalogram (MI-EEG) decoding plays a crucial role in developing motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI remains challenging due to the inherent complexity of EEG signals relative
Externí odkaz:
http://arxiv.org/abs/2407.03177
Autor:
Wu, Chengyu, Wang, Chengkai, Wang, Yaqi, Zhou, Huiyu, Zhang, Yatao, Wang, Qifeng, Wang, Shuai
Esophageal cancer is one of the most common types of cancer worldwide and ranks sixth in cancer-related mortality. Accurate computer-assisted diagnosis of cancer progression can help physicians effectively customize personalized treatment plans. Curr
Externí odkaz:
http://arxiv.org/abs/2405.09539
Autor:
Guo, Taicheng, Chen, Xiuying, Wang, Yaqi, Chang, Ruidi, Pei, Shichao, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently, based on th
Externí odkaz:
http://arxiv.org/abs/2402.01680