The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for...

Area of Science:

• Medical image analysis
• Digital pathology
• Deep learning applications

Background:

• Deep learning frameworks are now the primary methodology for medical image analysis.
• Deep learning excels at complex patterns, making it ideal for digital pathology tasks.
• Common tasks include classification, detection, and segmentation of pathological features.

Purpose of the Study:

• To review pre- and post-processing methods used in deep learning for digital pathology.
• To highlight how these methods optimize input preparation and refine network output.
• To discuss the broader applicability of these techniques beyond pathology.

Main Methods:

• Review of traditional image processing techniques integrated into deep learning pipelines.
• Focus on pre-processing for input optimization and post-processing for result enhancement.
• Analysis of methods applied within deep neural network frameworks.

Main Results:

• Integration of pre- and post-processing significantly boosts deep learning model performance.
• These methods simplify complex image analysis tasks like classification, detection, and segmentation.
• Many techniques are transferable to other image analysis domains.

Conclusions:

• Pre- and post-processing are crucial for maximizing deep learning effectiveness in digital pathology.
• These integrated approaches offer substantial performance gains over standalone deep neural networks.
• The presented methods have wide-ranging utility across various image analysis fields.