Zobrazeno 1 - 10
of 596
pro vyhledávání: '"Chen Xiwen"'
Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis
Despite recent progress in reducing road fatalities, the persistently high rate of traffic-related deaths highlights the necessity for improved safety interventions. Leveraging large-scale graph-based nationwide road network data across 49 states in
Externí odkaz:
http://arxiv.org/abs/2411.02542
In recent years, significant progress has been made in the medical image analysis domain using convolutional neural networks (CNNs). In particular, deep neural networks based on a U-shaped architecture (UNet) with skip connections have been adopted f
Externí odkaz:
http://arxiv.org/abs/2410.11578
Autor:
Dong, Xuanzhao, Vasa, Vamsi Krishna, Zhu, Wenhui, Qiu, Peijie, Chen, Xiwen, Su, Yi, Xiong, Yujian, Yang, Zhangsihao, Chen, Yanxi, Wang, Yalin
Retinal fundus photography is significant in diagnosing and monitoring retinal diseases. However, systemic imperfections and operator/patient-related factors can hinder the acquisition of high-quality retinal images. Previous efforts in retinal image
Externí odkaz:
http://arxiv.org/abs/2409.10966
Autor:
Wang, Hao, Zhu, Wenhui, Qin, Jiayou, Li, Xin, Dumitrascu, Oana, Chen, Xiwen, Qiu, Peijie, Razi, Abolfazl
Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for this purpose often are coarse-level and lack fine-grained analysis for
Externí odkaz:
http://arxiv.org/abs/2407.12271
Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detecting tumorous lesions. However, existing mainstream MIL methods focus on
Externí odkaz:
http://arxiv.org/abs/2407.03575
Autor:
Zhu, Wenhui, Chen, Xiwen, Qiu, Peijie, Farazi, Mohammad, Sotiras, Aristeidis, Razi, Abolfazl, Wang, Yalin
Since its introduction, UNet has been leading a variety of medical image segmentation tasks. Although numerous follow-up studies have also been dedicated to improving the performance of standard UNet, few have conducted in-depth analyses of the under
Externí odkaz:
http://arxiv.org/abs/2406.14896
Autor:
Chen, Xiwen, Qiu, Peijie, Zhu, Wenhui, Li, Huayu, Wang, Hao, Sotiras, Aristeidis, Wang, Yalin, Razi, Abolfazl
Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC). However, these methods often rely on supervised learning, which does not fully account for the
Externí odkaz:
http://arxiv.org/abs/2405.03140
Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed for being under-determined with multiple interchangeably consistent so
Externí odkaz:
http://arxiv.org/abs/2405.02944
Autor:
Wang, Hao, Qin, Jiayou, Chen, Xiwen, Bastola, Ashish, Suchanek, John, Gong, Zihao, Razi, Abolfazl
Assistive visual navigation systems for visually impaired individuals have become increasingly popular thanks to the rise of mobile computing. Most of these devices work by translating visual information into voice commands. In complex scenarios wher
Externí odkaz:
http://arxiv.org/abs/2404.17031
Many AI platforms, including traffic monitoring systems, use Federated Learning (FL) for decentralized sensor data processing for learning-based applications while preserving privacy and ensuring secured information transfer. On the other hand, apply
Externí odkaz:
http://arxiv.org/abs/2403.17331