Deep learning a boon for biophotonics?

Area of Science:

• Biophotonics
• Machine Learning
• Artificial Intelligence

Background:

• Deep learning, a subset of machine learning, has emerged as a powerful tool in biophotonics.
• Recent years have seen its application across various biophotonic tasks, achieving state-of-the-art performance.

Purpose of the Study:

• To review original articles on deep learning applications in biophotonics.
• To discuss the potential of deep learning for image and spectroscopic data analysis in biophotonics.
• To identify future applications and challenges of deep learning in this field.

Main Methods:

• Review of original research articles published in recent years.
• Analysis of deep learning methodologies, particularly artificial neural network architectures.
• Categorization of applications in image processing (classification, segmentation, registration, pseudostaining, resolution enhancement) and spectroscopic data analysis (preprocessing, classification).

Main Results:

• Deep learning has achieved state-of-the-art performance in numerous biophotonic tasks.
• The field is rapidly growing, with increasing utilization of deep learning techniques.
• Deep learning holds promise for real-time biophotonic decision-making systems.

Conclusions:

• Deep learning offers significant potential for advancing biophotonic data analysis.
• Future applications include enhanced image analysis and sophisticated spectroscopic data interpretation.
• Addressing challenges is crucial for the widespread adoption of deep learning in biophotonics.