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GJFocuser: a Gaussian difference and joint learning-based autofocus method for whole slide imaging.

Wujie Chen¹, Caiwei Li², Zhen-Li Huang²

¹School of Computer Science and Technology, Hainan University, Haikou 570228, China.

Biomedical Optics Express

|January 16, 2025

Summary

This summary is machine-generated.

This study introduces a novel autofocusing method for whole slide imaging (WSI) using difference between Gaussians (DoG) and joint learning. The technique improves accuracy despite staining and sample variations, enhancing diagnostic system quality.

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Area of Science:

Digital pathology
Computational imaging
Biomedical engineering

Background:

Whole slide imaging (WSI) is crucial for computer-aided diagnostics, requiring high-precision autofocusing.
Existing autofocusing methods struggle with staining variations and sample heterogeneity in WSI.

Purpose of the Study:

To develop a robust autofocusing method for WSI that overcomes limitations of current techniques.
To enhance the quality and reliability of WSI for diagnostic applications.

Main Methods:

Proposed a novel autofocusing method combining difference between Gaussians (DoG) and joint learning.
DoG enhances edge information, reducing sensitivity to staining variations.
Joint learning constrains network sensitivity to defocus, addressing sample morphology differences.

Main Results:

Achieved state-of-the-art performance in comparative experiments on public datasets.
Demonstrated effectiveness and versatility in a low-cost digital microscopy system.
The method shows robustness against staining variations and sample heterogeneity.

Conclusions:

The proposed DoG and joint learning autofocusing method significantly improves WSI quality.
This approach offers a robust and versatile solution for digital pathology and computer-aided diagnostics.
Enables reliable cellular-level tissue visualization for enhanced diagnostic accuracy.