Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA.
Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA.
Phys Chem Chem Phys. 2024 Apr 17;26(15):11263-11270. doi: 10.1039/d3cp05673b.
Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct utilization in ML algorithms without the need for additional processing. Additionally, AFM enables the simultaneous imaging of distributions of over a dozen different physicochemical properties of sample surfaces, a process known as multidimensional imaging. While this wealth of information can be challenging to analyze using traditional methods, ML provides a seamless approach to this task. However, the relatively slow speed of AFM imaging poses a challenge in applying deep learning methods broadly used in image recognition. This prospective is focused on ML recognition/classification when using a relatively small number of AFM images, small database. We discuss ML methods other than popular deep-learning neural networks. The described approach has already been successfully used to analyze and classify the surfaces of biological cells. It can be applied to recognize medical images, specific material processing, in forensic studies, even to identify the authenticity of arts. A general template for ML analysis specific to AFM is suggested, with a specific example of the identification of cell phenotype. Special attention is given to the analysis of the statistical significance of the obtained results, an important feature that is often overlooked in papers dealing with machine learning. A simple method for finding statistical significance is also described.
原子力显微镜（AFM 或 SPM）成像技术是与显微镜技术中机器学习（ML）分析最匹配的技术之一。AFM 图像的数字格式允许直接在 ML 算法中使用，而无需进行额外的处理。此外，AFM 还能够同时对样品表面的十几种不同理化性质的分布进行成像，这一过程称为多维成像。虽然使用传统方法分析这些丰富的信息可能具有挑战性，但 ML 为这项任务提供了一种无缝的方法。然而，AFM 成像的相对较慢的速度在广泛应用于图像识别的深度学习方法中存在挑战。本研究主要关注在使用相对较少的 AFM 图像、较小的数据库时，ML 识别/分类。我们讨论了除了流行的深度学习神经网络之外的 ML 方法。该方法已经成功地用于分析和分类生物细胞的表面。它可以应用于识别医学图像、特定材料处理、法医研究，甚至识别艺术品的真伪。我们提出了一个针对 AFM 的 ML 分析通用模板，并以细胞表型识别为例进行了说明。特别关注对获得结果的统计显著性的分析，这是在涉及机器学习的论文中经常被忽视的一个重要特征。还描述了一种寻找统计显著性的简单方法。
