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雷伊-奥斯特里赫复杂图形测试自动评分的一个基准。

A benchmark for Rey-Osterrieth complex figure test automatic scoring.

作者信息

Guerrero-Martín Juan, Díaz-Mardomingo María Del Carmen, García-Herranz Sara, Martínez-Tomás Rafael, Rincón Mariano

机构信息

Department of Artificial Intelligence, UNED, Madrid, Spain.

Department of Basic Psychology I, UNED, Madrid, Spain.

出版信息

Heliyon. 2024 Oct 29;10(21):e39883. doi: 10.1016/j.heliyon.2024.e39883. eCollection 2024 Nov 15.

Abstract

The Rey-Osterrieth complex figure (ROCF) test is a neuropsychological task that can be useful for early detection of cognitive decline in the elderly population. Several computer vision systems have been proposed to automate this complex analysis task, but the lack of public benchmarks does not allow a fair comparison of these systems. To advance in that direction, we present a benchmarking framework for the automatic scoring of the ROCF test that provides: the ROCFD528 dataset, which is the first open dataset of ROCF line drawings; and experimental results obtained by several modern deep learning models, which can be used as a baseline for comparing new proposals. We evaluate different state-of-the-art convolutional neural networks (CNNs) under traditional and transfer learning paradigms. Experimental quantitative results (MAE = 3.448) indicate that a CNN specifically designed for sketches outperforms other state of the art CNN architectures when the number of examples available is limited. This benchmark can also be a paradigmatic example within the broad field of machine learning for the development of efficient and robust models for analyzing line drawings and sketches not only in classification but also in regression tasks.

摘要

雷-奥斯特里茨复杂图形(ROCF)测试是一项神经心理学任务,有助于早期发现老年人群的认知衰退。已经提出了几种计算机视觉系统来自动化这项复杂的分析任务,但由于缺乏公开基准,无法对这些系统进行公平比较。为了朝这个方向前进,我们提出了一个用于ROCF测试自动评分的基准框架,该框架提供:ROCFD528数据集,这是第一个关于ROCF线条图的开放数据集;以及几个现代深度学习模型获得的实验结果,这些结果可作为比较新提议的基线。我们在传统和迁移学习范式下评估不同的先进卷积神经网络(CNN)。实验定量结果(平均绝对误差=3.448)表明,当可用示例数量有限时,专门为草图设计的CNN优于其他先进的CNN架构。这个基准也可以成为机器学习广泛领域中的一个范例,用于开发高效且强大的模型,不仅用于分析线条图和草图的分类任务,还用于回归任务。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc20/11566690/ceb314a4e064/gr001.jpg

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