用神经色彩迁移提升血涂片染色标准化，增强模型泛化能力。
Stain Normalization of Hematology Slides using Neural Color Transfer
- 采用神经色彩迁移技术实现染色风格统一
- 在未训练样本上白细胞检测准确率显著提升
- 适合需要跨设备、跨批次病理图像分析的研究者
深度学习广泛用于病理图像分析，但图像属性差异会限制模型效果。本研究旨在开发一种方法，将训练集中的变化特性迁移到未见图像中，提升模型推理准确性。使用YOLOv5在周围血和骨髓样本图像上进行训练，并结合神经色彩迁移技术引入不变性。结果表明，经过归一化处理后，在未训练样本上的白细胞（WBCs）检测性能显著改善，凸显了基于深度学习的归一化技术在提升推理鲁棒性方面的潜力。
原文摘要 · Abstract (English)
Deep learning is popularly used for analyzing pathology images, but variations in image properties can limit the effectiveness of the models. The study aims to develop a method that transfers the variability present in the training set to unseen images, improving the model's ability to make accurate inferences. YOLOv5 was trained on peripheral blood and bone marrow sample images and Neural Color Transfer techniques were used to incorporate invariance. The results showed significant improvement in detecting WBCs from untrained samples after normalization, highlighting the potential of deep learning-based normalization techniques for inference robustness.
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