Rakhimberdina Zarina, Jodelet Quentin, Liu Xin, Murata Tsuyoshi
Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan.
AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo, Japan.
Front Neurosci. 2021 Dec 20;15:795488. doi: 10.3389/fnins.2021.795488. eCollection 2021.
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain. One of the most challenging brain decoding tasks is the accurate reconstruction of the perceived natural images from brain activities measured by functional magnetic resonance imaging (fMRI). In this work, we survey the most recent deep learning methods for natural image reconstruction from fMRI. We examine these methods in terms of architectural design, benchmark datasets, and evaluation metrics and present a fair performance evaluation across standardized evaluation metrics. Finally, we discuss the strengths and limitations of existing studies and present potential future directions.
随着脑成像技术和机器学习工具的出现，人们投入了大量精力来构建计算模型，以捕捉人类大脑中视觉信息的编码。最具挑战性的脑解码任务之一是根据功能磁共振成像（fMRI）测量的大脑活动准确重建感知到的自然图像。在这项工作中，我们综述了从fMRI重建自然图像的最新深度学习方法。我们从架构设计、基准数据集和评估指标等方面对这些方法进行了研究，并通过标准化评估指标给出了公平的性能评估。最后，我们讨论了现有研究的优势和局限性，并提出了潜在的未来研究方向。
