Color Augmentation Strategies for Improving Convolutional Neural Network Robustness in Medical Tissue Classification

Abstract

Accurate classification of histological tissue specimens using convolutional neural networks (CNNs) is critically hindered by inter-laboratory color variability arising from heterogeneous staining protocols, slide preparation techniques, and digital scanning equipment. Such chromatic inconsistencies impose severe distributional shifts between training and deployment domains, substantially degrading model generalization and clinical reliability. Addressing this fundamental challenge, the present work systematically investigates a comprehensive suite of color augmentation strategies designed to enhance CNN robustness across diverse tissue classification tasks.

We propose and rigorously evaluate a structured taxonomy of color augmentation techniques, encompassing classical photometric transformations—including random hue rotation, saturation perturbation, and brightness-contrast jitter—alongside stain-space augmentations operating within the Haematoxylin-Eosin (H&E) optical density domain via Macenko and Vahadane decomposition methods. Additionally, we introduce a novel stochastic stain normalization augmentation pipeline that probabilistically interpolates between source and target stain distributions during training, explicitly exposing learned representations to realistic chromatic variation.

Extensive experiments are conducted on three publicly available histopathology benchmarks—spanning colorectal polyp grading, breast carcinoma subtyping, and lung tissue classification—utilizing ResNet-50, EfficientNet-B3, and a lightweight vision transformer as backbone architectures. Quantitative evaluation demonstrates that targeted stain-space augmentation strategies yield statistically significant improvements in classification accuracy and macro-averaged F1 score, with gains of up to 6.3 percentage points over non-augmented baselines under cross-domain evaluation protocols.

Our findings establish concrete, evidence-based recommendations for color augmentation selection in computational pathology pipelines, demonstrating that domain-informed chromatic perturbation substantially outperforms generic photometric augmentation. The proposed methodology provides practical guidance for developing clinically robust, stain-invariant tissue classifiers deployable across heterogeneous laboratory environments.