StainPresetNet解决了染色标准化中的方向限制问题，计算效率提升90%，适合病理图像分析。
StainPresetNet是一种新型染色标准化框架，结合结构保留与数据集级颜色映射。该方法在细胞病理学和组织病理学数据集上评估，相比传统方法实现更优的颜色映射准确度。与现有深度学习方法相比，计算开销降低90%。通过预设参考图像引导像素级标准化，无需重新训练即可实现多方向适应性。
StainPresetNet: Stain Preset Network for Fast Multi-to-Multi Stain Normalization
Stain normalization reduces color variations caused by variations in staining protocols and imaging conditions, thereby enhancing computer-aided diagnostic system performance. Traditional methods derive mapping relationships from individual or limited reference images through pixel-wise transformation, offering style flexibility but suffering from inaccurate color mapping extraction. While existing deep-learning-based approaches achieve accurate dataset-wide color mapping through complex neural networks, they face challenges including computational inefficiency, artifact generation, and fixed normalization directions requiring model retraining for directional changes. To address these limitations, we propose StainPresetNet - a novel framework that combines structural preservation with dataset-level color mapping while maintaining computational efficiency. Our method implements pixel-wise normalization guided by preset reference images, enabling multi-directional adaptability without retraining. Evaluations on cytopathology and histopathology datasets demonstrate that StainPresetNet achieves superior color mapping accuracy compared to conventional methods, effectively improves classifier generalization in diagnostic tasks, and reduces computational overhead by 90\% versus existing deep learning approaches. The proposed preset-guided mechanism facilitates flexible adjustment of normalization directions through simple reference image replacement, overcoming the directional rigidity of current deep-learning-based solutions.