Abstract
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.
Abstract (translated)
原子力显微镜（AFM或SPM）成像是一种在显微镜技术中与机器学习（ML）分析的最佳匹配。AFM图像的数字格式允许直接应用于ML算法，而不需要进行额外处理。此外，AFM能够同时成像样本表面超过十几种不同的物理化学性质，这是一种称为多维成像的过程。虽然这些信息使用传统方法分析起来可能具有挑战性，但ML为这项任务提供了顺利的方法。然而，AFM成像的速度相对较慢，这使得广泛应用于图像识别的深度学习方法在应用上具有一定的限制。本研究专注于使用相对较小的AFM图像进行ML识别/分类。我们讨论了除流行深度学习神经网络之外的其他ML方法。已经成功应用于分析生物细胞的表面。该方法可用于识别医学图像、特定材料处理和法医研究，甚至可用于识别艺术品的原真性。针对AFM的ML分析的具体指导建议，并给出一个具体的细胞表型识别的例子。特别关注分析所得结果的统计显著性，这是在机器学习论文中经常被忽视的重要特征。还描述了一种简单的方法来查找统计显著性。
URL
https://arxiv.org/abs/2403.16230
