Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning

Elizaveta Chechekhina

doi:10.1089/ten.tea.2024.0014

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning

2024-10-01

1

PRE

AI

E

Elizaveta Chechekhina *

N

Nikita Voloshin

K

Konstantin Kulebyakin

P

Pyotr A. Tyurin‐Kuzmin

DOI：10.1089/ten.tea.2024.0014

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In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases. Impact Statement Impact statement In our article, we invite researchers to familiarize themselves with the principles of operation of widely used deep learning algorithms in the analysis of microscopic images and the most convenient and advanced open examples of their no-code application. A significant advantage of our review is that we help our colleagues understand the mechanisms of algorithm operation using simple language, devoid of complex mathematical terms and formulas, without delving into programming intricacies. Thus, a researcher who reads our review will be able to grasp the basic mechanics of the algorithm of interest and find a specific solution without requiring a substantial amount of time to master it.

Keyword:

deep learning

open-source code-free microscopy image analysis

artificial intelligence

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning

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