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A systematic evaluation of grayscale conversion methods for mitigating color variation in deep learning-based

Napat Srisermphoak¹, Panomwat Amornphimoltham², Risa Chaisuparat³

¹Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand.

Journal of Pathology Informatics

|March 27, 2026

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Grayscale conversion standardizes histopathological images for deep learning (DL), improving performance across different staining and scanning methods. Novel methods like ACSRM enhance DL generalization for reliable clinical use.

Area of Science:

Digital pathology
Machine learning in healthcare
Computational imaging

Background:

Deep learning (DL) for histopathological analysis faces challenges due to color variations from staining and scanning.
Morphological features in hematoxylin and eosin slides are crucial for diagnosis.
Standardizing input data is essential for robust DL model performance.

Purpose of the Study:

To investigate grayscale conversion as a method to standardize histopathological images for DL analysis.
To evaluate the performance of various grayscale algorithms against RGB color images.
To introduce and assess a novel attention-based grayscale conversion method (ACSRM).

Main Methods:

Evaluated six grayscale algorithms and RGB across single-center, mixed-center, cross-scanner, and cross-center generalization tests.

Keywords:

Color variation Deep learning Digital pathology Grayscale Image classification Image segmentation

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Introduced and tested an attention-based grayscale conversion method (ACSRM) using transformer attention mechanisms.

Utilized statistical tests (Wilcoxon signed-rank, McNemar's) to compare model performance and decision behaviors.

Main Results:

Grayscale methods achieved performance comparable to RGB in homogeneous settings.
In mixed-center training, grayscale algorithms outperformed RGB in several models, showing distinct decision behaviors.
ACSRM and Luster demonstrated superior generalization under distribution-shift scenarios (cross-scanner and cross-center tests), significantly improving F1-scores.

Conclusions:

Grayscale conversion is an effective strategy for standardizing histopathological images, mitigating color variations.
The novel ACSRM and Luster methods show significant promise for enhancing DL generalization in digital pathology.
These findings facilitate the reliable clinical deployment of deep learning in histopathology.