DeepHistoViT: 一种用于病理癌症分类的可解释视觉Transformer框架
DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification
AI总结：
DeepHistoViT通过定制的Transformer架构和注意力机制，实现了对组织病理学图像的高精度分类，展现出在癌症诊断中的卓越性能和可解释性。
摘要：
组织病理学仍然是癌症诊断的黄金标准，因为它能提供组织形态的详细细胞级评估。然而，手动组织病理学检查耗时、劳动强度大且受观察者间变异影响，催生了对可靠计算机辅助诊断工具的需求。近年来，深度学习，特别是基于Transformer的架构，在建模医学图像中的复杂空间依赖性方面展现出强大潜力。在本工作中，我们提出了DeepHistoViT，一种用于自动分类组织病理学图像的基于Transformer的框架。该模型采用定制化的视觉Transformer架构，并集成了一个设计用于捕捉细粒度细胞结构的注意力机制，同时通过基于注意力的定位来提高诊断相关区域的可解释性。该框架在三个公开可用的组织病理学数据集上进行了评估，涵盖肺癌、结肠癌和急性淋巴细胞白血病。实验结果表明，在所有数据集上均实现了最先进的性能，分类准确率、精确率、召回率、F1分数和ROC-AUC在肺癌和结肠癌数据集上达到100%，在急性淋巴细胞白血病数据集上分别达到99.85%、99.84%、99.86%、99.85%和99.99%。所有性能指标均以95%置信区间报告。这些结果突显了基于Transformer的架构在组织病理学图像分析中的有效性，并展示了DeepHistoViT作为可解释的计算机辅助诊断工具的潜力，以支持病理学家在临床决策中的作用。
英文摘要：
Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive, and subject to inter-observer variability, creating a demand for reliable computer-assisted diagnostic tools. Recent advances in deep learning, particularly transformer-based architectures, have shown strong potential for modelling complex spatial dependencies in medical images. In this work, we propose DeepHistoViT, a transformer-based framework for automated classification of histopathological images. The model employs a customized Vision Transformer architecture with an integrated attention mechanism designed to capture fine-grained cellular structures while improving interpretability through attention-based localization of diagnostically relevant regions. The framework is evaluated on three publicly available histopathology datasets covering lung cancer, colon cancer, and acute lymphoblastic leukaemia. Experimental results demonstrate state-of-the-art performance across all datasets, with classification accuracy, precision, recall, F1-score, and ROC-AUC reaching 100 percent on the lung and colon cancer datasets, and 99.85 percent, 99.84 percent, 99.86 percent, 99.85 percent, and 99.99 percent respectively on the acute lymphoblastic leukaemia dataset. All performance metrics are reported with 95 percent confidence intervals. These results highlight the effectiveness of transformer-based architectures for histopathological image analysis and demonstrate the potential of DeepHistoViT as an interpretable computer-assisted diagnostic tool to support pathologists in clinical decision-making.
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