Dinsdale Nicola K, Bluemke Emma, Sundaresan Vaanathi, Jenkinson Mark, Smith Stephen M, Namburete Ana I L
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Machine Learning in NeuroImaging Lab, OMNI, Department of Computer Science, University of Oxford, Oxford, UK.
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Neuron. 2022 Dec 7;110(23):3866-3881. doi: 10.1016/j.neuron.2022.09.012. Epub 2022 Oct 10.
Combining deep learning image analysis methods and large-scale imaging datasets offers many opportunities to neuroscience imaging and epidemiology. However, despite these opportunities and the success of deep learning when applied to a range of neuroimaging tasks and domains, significant barriers continue to limit the impact of large-scale datasets and analysis tools. Here, we examine the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation, and logistical challenges and discuss the problems that still need to be tackled to enable the success of "big data" deep learning approaches beyond research.
将深度学习图像分析方法与大规模成像数据集相结合，为神经科学成像和流行病学带来了诸多机遇。然而，尽管存在这些机遇，且深度学习在一系列神经成像任务和领域中取得了成功，但重大障碍仍然限制着大规模数据集和分析工具的影响力。在此，我们审视了主要挑战以及为克服这些挑战而探索的方法。我们聚焦于与数据可用性、可解释性、评估以及后勤挑战相关的问题，并讨论为实现“大数据”深度学习方法在研究之外取得成功仍需解决的问题。
