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EVENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRI

Autor: Li, Chenjun, Yang, Dian, Yao, Shun, Wang, Shuyue, Wu, Ye, Zhang, Le, Li, Qiannuo, Cho, Kang Ik Kevin, Seitz-Holland, Johanna, Ning, Lipeng, Legarreta, Jon Haitz, Rathi, Yogesh, Westin, Carl-Fredrik, O'Donnell, Lauren J., Sochen, Nir A., Pasternak, Ofer, Zhang, Fan

In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the design of an evidential deep learning framework to quantify predictive un

Externí odkaz: http://arxiv.org/abs/2409.07020

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Application Research On Real-Time Perception Of Device Performance Status

Autor: Wang, Zhe, Wang, Zhen, Wu, Jianwen, Xiao, Wangzhong, Chen, Yidong, Feng, Zihua, Yang, Dian, Liu, Hongchen, Liang, Bo, Fu, Jiaojiao

In order to accurately identify the performance status of mobile devices and finely adjust the user experience, a real-time performance perception evaluation method based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) comb

Externí odkaz: http://arxiv.org/abs/2409.03218

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