Multimodal data fusion in neuroscience: promises, challenges and future directions [0.03%]
神经科学中的多模态数据融合：希望、挑战和未来方向
Chuang Liang,Rogers F Silva,TulayAdali et al.
Chuang Liang et al.
Multimodal fusion provides significant benefits over single modality analysis by leveraging both shared and complementary information across diverse data sources. In this article, we systematically review methods for fusion of heterogonous ...
The Marriage of Neurotechnologies and Artificial Intelligence: Ethical, regulatory, and technological aspects [0.03%]
神经技术与人工智能的融合：伦理、监管和技术方面的问题
C Chiurco,A Favaro,S F Storti et al.
C Chiurco et al.
The dual concepts of neurotechnology and artificial intelligence (AI) form an intriguing but also potentially explosive mixture because of its many ethical and legal implications. The advent of AI and the progress in neurotechnologies are r...
Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities [0.03%]
幼儿自然记录的自动化分析：应用、挑战与机遇
Jialu Li,Marvin Lavechin,Xulin Fan et al.
Jialu Li et al.
Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic long-form recordings extend this concept by capturing spontan...
Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits [0.03%]
基于领域随机化的深度学习在神经影像分析中的应用：选择训练策略、应对挑战及最大化收益
Malte Hoffmann
Malte Hoffmann
Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute ...
Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning [0.03%]
基于生成式AI和深度表征学习的脑控内容新兴技术
Zhe Sage Chen
Zhe Sage Chen
Rapid advances in generative artificial intelligence (AI) and deep representation learning have revolutionized numerous engineering applications in signal processing, computer vision, speech recognition and translation, and natural language...
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey [0.03%]
基于物理/模型和数据驱动的低剂量CT方法概述
Wenjun Xia,Hongming Shan,Ge Wang et al.
Wenjun Xia et al.
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction ne...
High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions [0.03%]
基于物理建模和数据驱动机器学习的高维磁共振时空谱成像：当前进展与未来方向
Fan Lam,Xi Peng,Zhi-Pei Liang
Fan Lam
Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical...
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging [0.03%]
基于物理的深度学习在计算磁共振成像中的应用：结合物理和机器学习以改善医学影像学
Kerstin Hammernik,Thomas Küstner,Burhaneddin Yaman et al.
Kerstin Hammernik et al.
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments...
Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging [0.03%]
基于机器学习的预测神经影像解释中的挑战和解决方案：解读大脑生物标志物
Rongtao Jiang,Choong-Wan Woo,Shile Qi et al.
Rongtao Jiang et al.
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding ...