神经影像学
变压器
理解力
计算机科学
人工智能
机器学习
数据科学
认知科学
心理学
神经科学
工程类
电压
电气工程
程序设计语言
作者
Xinyu Zhu,Shen Sun,Lan Lin,Yutong Wu,Xiangge Ma
标识
DOI:10.1515/revneuro-2024-0088
摘要
Abstract In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a formidable neural network architecture, gaining significant traction in neuroimaging-based classification and regression tasks. This paper presents an extensive examination of transformer’s application in neuroimaging, surveying recent literature to elucidate its current status and research advancement. Commencing with an exposition on the fundamental principles and structures of the transformer model and its variants, this review navigates through the methodologies and experimental findings pertaining to their utilization in neuroimage classification and regression tasks. We highlight the transformer model’s prowess in neuroimaging, showcasing its exceptional performance in classification endeavors while also showcasing its burgeoning potential in regression tasks. Concluding with an assessment of prevailing challenges and future trajectories, this paper proffers insights into prospective research directions. By elucidating the current landscape and envisaging future trends, this review enhances comprehension of transformer’s role in neuroimaging tasks, furnishing valuable guidance for further inquiry.
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