Zobrazeno 1 - 10
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pro vyhledávání: '"Zhang, YunLong"'
Autor:
Li, Honglin, Sun, Yusuan, Zhu, Chenglu, Zhang, Yunlong, Zhang, Shichuan, Shui, Zhongyi, Chen, Pingyi, Li, Jingxiong, Zheng, Sunyi, Cui, Can, Yang, Lin
Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via cytology Whole Slide Image (WSI) diagnosis is critical to prevent this Cancer progression and im
Externí odkaz:
http://arxiv.org/abs/2407.19512
Autor:
Zhao, Zhiyu, Liu, Qibin, Chen, Jiyuan, Chen, Jing, Chen, Junfeng, Chen, Xiang, Fu, Changbo, Guo, Jun, Khaw, Kim Siang, Li, Liang, Li, Shu, Liu, Danning, Liu, Kun, Song, Siyuan, Sun, Tong, Tang, Jiannan, Wang, Yufeng, Wang, Zhen, Wu, Weihao, Yang, Haijun, Lin, Yuming, Yuan, Rui, Zhang, Yulei, Zhang, Yunlong, Zhou, Baihong, Zhu, Xuliang, Zhu, Yifan
This paper presents the design and optimization of a LYSO crystal-based electromagnetic calorimeter (ECAL) for the DarkSHINE experiment, which aims to search for dark photon as potential dark force mediator. The ECAL design has been meticulously eval
Externí odkaz:
http://arxiv.org/abs/2407.17800
Autor:
Sun, Yuxuan, Zhang, Yunlong, Si, Yixuan, Zhu, Chenglu, Shui, Zhongyi, Zhang, Kai, Li, Jingxiong, Lyu, Xingheng, Lin, Tao, Yang, Lin
Vision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Slide Image (WSI) analysis. Additionally, they can function as vision en
Externí odkaz:
http://arxiv.org/abs/2407.00203
Autor:
Zhang, Yunlong, Shui, Zhongyi, Sun, Yunxuan, Li, Honglin, Li, Jingxiong, Zhu, Chenglu, Yang, Lin
Multiple Instance Learning (MIL) has demonstrated effectiveness in analyzing whole slide images (WSIs), yet it often encounters overfitting challenges in real-world applications, particularly in the form of attention over-concentration. While existin
Externí odkaz:
http://arxiv.org/abs/2406.15303
Autor:
Sun, Yuxuan, Wu, Hao, Zhu, Chenglu, Zheng, Sunyi, Chen, Qizi, Zhang, Kai, Zhang, Yunlong, Wan, Dan, Lan, Xiaoxiao, Zheng, Mengyue, Li, Jingxiong, Lyu, Xinheng, Lin, Tao, Yang, Lin
The emergence of large multimodal models has unlocked remarkable potential in AI, particularly in pathology. However, the lack of specialized, high-quality benchmark impeded their development and precise evaluation. To address this, we introduce Path
Externí odkaz:
http://arxiv.org/abs/2401.16355
While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks. These inc
Externí odkaz:
http://arxiv.org/abs/2401.15193
Autor:
Zheng, Sunyi, Cui, Xiaonan, Sun, Yuxuan, Li, Jingxiong, Li, Honglin, Zhang, Yunlong, Chen, Pingyi, Jing, Xueping, Ye, Zhaoxiang, Yang, Lin
Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pretraining (CLIP) model has shown remarkable proficiency in understanding natural images. Drawi
Externí odkaz:
http://arxiv.org/abs/2401.02651
Autor:
Shui, Zhongyi, Zhang, Yunlong, Yao, Kai, Zhu, Chenglu, Zheng, Sunyi, Li, Jingxiong, Li, Honglin, Sun, Yuxuan, Guo, Ruizhe, Yang, Lin
Nucleus instance segmentation in histology images is crucial for a broad spectrum of clinical applications. Current dominant algorithms rely on regression of nuclear proxy maps. Distinguishing nucleus instances from the estimated maps requires carefu
Externí odkaz:
http://arxiv.org/abs/2311.15939
Histopathology image analysis is the golden standard of clinical diagnosis for Cancers. In doctors daily routine and computer-aided diagnosis, the Whole Slide Image (WSI) of histopathology tissue is used for analysis. Because of the extremely large s
Externí odkaz:
http://arxiv.org/abs/2311.12885
Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge. To improve the model's generalization ability to the new domain during eva
Externí odkaz:
http://arxiv.org/abs/2311.07877