卷积、Transformer和混合深度学习模型在结直肠组织学分类中的性能与可解释性
Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification
AI总结：
本研究系统比较了12种预训练CNN、Transformer和混合架构在结直肠组织病理学分类中的性能，发现EVA-02和ViT-B/16表现最佳，而现代CNN在准确率与模型复杂度间取得良好平衡。
摘要：
深度学习已成为计算病理学中的重要工具，能够实现组织病理学图像的自动分析。尽管卷积神经网络（CNN）传统上主导该领域，但基于Transformer和混合架构最近展现出有前景的性能。然而，这些方法在结直肠组织病理学中的全面比较仍然有限。本研究使用包含来自8种组织类别的5000张图像块的Kather结直肠组织病理学数据集，评估了12种ImageNet预训练的CNN、Transformer和混合架构。所有模型均采用标准化的迁移学习和微调协议进行训练，并使用多种性能指标进行评估，包括准确率、精确率、灵敏度、特异性、F1分数、ROC-AUC、Cohen's kappa和Matthews相关系数。所有评估模型均实现了高分类性能，准确率范围为93.2%至97.1%。EVA-02取得了最高的整体性能（准确率97.1%，F1分数97.0%），紧随其后的是ViT-B/16。在CNN中，ResNet34和ConvNeXt-Tiny表现出极具竞争力的性能，准确率分别达到96.4%和96.3%。Transformer架构在评估指标上通常产生最强结果，尽管最佳Transformer与CNN模型之间的性能差距相对较小。逐类别分析显示所有组织类别均具有持续强劲的分类性能，其中复杂基质是最具挑战性的类别。总体而言，基于Transformer的架构取得了最高的预测性能，而现代CNN在准确率与模型复杂度之间提供了有利的平衡。这些发现为结直肠组织病理学分类中的主要深度学习范式提供了全面基准。
英文摘要：
Deep learning has become an important tool in computational pathology, enabling automated analysis of histopathological images. While convolutional neural networks (CNNs) have traditionally dominated this field, transformer-based and hybrid architectures have recently demonstrated promising performance. However, comprehensive comparisons of these approaches for colorectal histopathology remain limited. This study evaluated twelve ImageNet-pretrained CNN, transformer, and hybrid architectures using the Kather colorectal histopathology dataset containing 5,000 image tiles from eight tissue classes. All models were trained using a standardized transfer-learning and fine-tuning protocol and assessed using multiple performance metrics, including accuracy, precision, sensitivity, specificity, F1-score, ROC-AUC, Cohen's kappa, and Matthews correlation coefficient. All evaluated models achieved high classification performance, with accuracies ranging from 93.2% to 97.1%. EVA-02 achieved the highest overall performance (97.1% accuracy, 97.0% F1-score), closely followed by ViT-B/16. Among CNNs, ResNet34 and ConvNeXt-Tiny demonstrated highly competitive performance, achieving accuracies of 96.4% and 96.3%, respectively. Transformer architectures generally produced the strongest results across evaluation metrics, although the performance gap between the best transformer and CNN models was relatively small. Per-class analysis showed consistently strong classification performance across all tissue categories, with Complex Stroma representing the most challenging class. Overall, transformer-based architectures achieved the highest predictive performance, whereas modern CNNs provided a favorable balance between accuracy and model complexity. These findings provide a comprehensive benchmark of major deep learning paradigms for colorectal histopathology classification.
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