ArticleMethods in molecular biology (Clifton, N.J.)2026
Artificial Neural Networks for Bioimage Analysis.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
Quantitative optical microscopy has grown to become an accepted methodology in biology and biomedical research labs. This technique has enabled new biological discoveries by relying on the computational analysis of microscopy datasets to provide a detailed look at the complex behavior and interactions of individual cells and molecules. However, as modern microscopy techniques evolve, the resulting datasets increase in both size and complexity, which has led to difficulties in scaling up with traditional analytical methods. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as promising solutions for bioimage analysis. In this chapter, we provide an introduction for researchers looking to implement AI/ML in their imaging pipelines, highlighting commonly used network architectures and models and their applications, and providing practical advice for their implementation and validation.
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