Machine learning in microscopy - insights, opportunities and challenges.

Area of Science:

• Life Science Research
• Microscopy Image Analysis
• Computational Biology

Background:

• Machine learning (ML) is revolutionizing image processing and analysis.
• Its application in microscopy offers significant potential for life science research.
• Understanding ML's role is crucial for advancing image-driven biological studies.

Purpose of the Study:

• To review the opportunities and challenges of applying ML pipelines to microscopy datasets.
• To guide users on selecting appropriate ML models based on data characteristics.
• To discuss the utility and potential pitfalls of ML in microscopy.

Main Methods:

• Review of ML applications in microscopy image analysis.
• Analysis of data characteristics (quantity, transferability, content) influencing ML model selection.
• Exploration of ML utility ranges: curation, exploration, prediction, and explanation.

Main Results:

• ML offers diverse applications in microscopy, including automation and pattern discovery.
• Data characteristics significantly impact the choice and outcome of ML models.
• ML utility spans data curation, exploration, prediction, and explanation in cell biology.

Conclusions:

• ML presents significant opportunities for microscopy but also poses challenges and risks.
• Careful consideration of data and model selection is essential for successful ML implementation.
• Mitigation strategies are proposed to address common ML artefacts and pitfalls in microscopy.