Deep-learning-based image compression for microscopy images: An empirical study

Area of Science:

• Computational Biology
• Bioimaging Data Science
• Artificial Intelligence in Microscopy

Background:

• Rapid advancements in microscopy generate massive bioimaging datasets, straining data infrastructure.
• Image compression is crucial for managing large volumes of imaging data.
• The impact of compression on downstream deep learning models remains an open question.

Purpose of the Study:

• To analyze and compare classic and deep learning-based image compression methods.
• To empirically study the impact of these compression techniques on downstream deep learning models.
• To evaluate compression effects on label-free prediction tasks using microscopy images.

Main Methods:

• Comparison of multiple classic and AI-based image compression algorithms.
• Empirical evaluation using deep learning-based label-free prediction models (bright-field to fluorescent image prediction).
• Analysis metrics included compression ratio, image similarity, and downstream model prediction accuracy.

Main Results:

• AI-based compression techniques demonstrated superior performance over classic methods.
• Deep learning compression showed minimal negative influence on the accuracy of 2D label-free prediction tasks.
• Compression ratio and image similarity varied significantly across different methods.

Conclusions:

• Deep learning-based image compression offers a promising solution for managing large bioimaging datasets.
• These advanced methods preserve data utility for critical downstream deep learning applications.
• Awareness of compression's impact on deep learning models is essential for bioimaging data analysis.