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

Yu Zhou^1,2, Jan Sollmann^1,2, Jianxu Chen¹

¹Department of Biospectroscopy, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.

Biological Imaging

|January 8, 2025

Summary

This summary is machine-generated.

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Artificial intelligence (AI)-based image compression significantly outperforms traditional methods for large bioimaging datasets. These advanced techniques minimize impact on downstream deep learning tasks, like label-free prediction, ensuring data integrity.

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.

Keywords:

compression deep learning in-silico labelling microscopic images

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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.