一种专家混合（MoE）模型，用于在组织病理学图像模糊程度变化的情况下提升基于AI的计算病理学预测性能
A Mixture of Experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of histopathology image blur
AI总结：
本文提出专家混合（MoE）策略，通过组合不同模糊水平数据训练的专家模型，显著提升组织病理学图像模糊条件下基于AI的预测性能，并在乳腺癌WSI分级和IHC预测任务中验证了其优于基线模型的效果。
摘要：
基于AI的组织病理学全切片图像（WSI）分析模型日益普遍，但WSI中不清晰或模糊的区域会显著降低预测性能。在本研究中，我们考察了图像模糊对深度学习模型的影响，并提出了一种专家混合（MoE）策略，该策略将多个在不同模糊水平数据上训练的专家模型的预测结果进行组合。我们使用来自2,093例乳腺癌患者的H&E染色WSI，在分级分类和IHC生物标志物预测任务上，对基于CNN（CNN_CLAM和MoE-CNN_CLAM）和基于Vision Transformer（UNI_CLAM和MoE-UNI_CLAM）的模型进行了基准测试。结果表明，基线模型的性能随模糊程度增加而持续下降，但在模糊图像块上训练的专家模型，尤其是我们提出的MoE方法，显著提升了性能，并在一系列模拟场景中优于基线模型。MoE-CNN_CLAM在中度模糊（AUC：0.868对0.702）和混合模糊条件（AUC：0.890对0.875）下优于基线CNN_CLAM。MoE-UNI_CLAM在中度模糊（AUC：0.950对0.928）和混合模糊条件（AUC：0.944对0.931）下均优于基线UNI_CLAM模型。该MoE方法有潜力在图像质量变化的情况下增强基于AI的病理学模型的可靠性，从而支持其在研究和临床环境中的更广泛应用。
英文摘要：
AI-based models for histopathology whole slide image (WSI) analysis are increasingly common, but unsharp or blurred areas within WSI can significantly reduce prediction performance. In this study, we investigated the effect of image blur on deep learning models and introduced a mixture of experts (MoE) strategy that combines predictions from multiple expert models trained on data with varying blur levels. Using H&E-stained WSIs from 2,093 breast cancer patients, we benchmarked performance on grade classification and IHC biomarker prediction with both CNN- (CNN_CLAM and MoE-CNN_CLAM) and Vision Transformer-based (UNI_CLAM and MoE-UNI_CLAM) models. Our results show that baseline models' performance consistently decreased with increasing blur, but expert models trained on blurred tiles and especially our proposed MoE approach substantially improved performance, and outperformed baseline models in a range of simulated scenarios. MoE-CNN_CLAM outperformed the baseline CNN_CLAM under moderate (AUC: 0.868 vs. 0.702) and mixed blur conditions (AUC: 0.890 vs. 0.875). MoE-UNI_CLAM outperformed the baseline UNI_CLAM model in both moderate (AUC: 0.950 vs. 0.928) and mixed blur conditions (AUC: 0.944 vs. 0.931). This MoE method has the potential to enhance the reliability of AI-based pathology models under variable image quality, supporting broader application in both research and clinical settings.
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