面向组织学切片级分类的神经网络校准研究
On the calibration of neural networks for histological slide-level classification
AI总结：
本研究针对数字病理中神经网络输出概率校准常被忽视的问题，对比三种切片级分类架构在结直肠癌MSI预测任务上的性能与校准效果，发现Transformer分类与泛化能力优但易过度自信。
摘要：
深度神经网络在数字病理领域从全切片图像（Whole Slide Images, WSI）中预测特定生物标志物时，已展现出可观的分类性能，但网络输出概率的校准效果通常未得到评估。在医疗场景中，通过提供可靠的置信度分数传递不确定性具有极高的相关性。本研究对比了三种将斑块级特征表征整合为切片级预测的神经网络架构的分类性能，并评估其校准效果。我们选取结直肠癌组织切片的微卫星不稳定性（Microsatellite Instability, MSI）预测作为切片级分类任务。研究发现，Transformer在分类性能和校准效果上均表现优异；在独立数据集上评估分类性能时，Transformer的泛化能力最佳。对可靠性图的分析为期望校准误差（Expected Calibration Error, ECE）指标提供了额外洞见，我们还发现尤其是Transformer会将输出概率推向极值，导致预测过度自信。
英文摘要：
Deep Neural Networks have shown promising classification performance when predicting certain biomarkers from Whole Slide Images in digital pathology. However, the calibration of the networks' output probabilities is often not evaluated. Communicating uncertainty by providing reliable confidence scores is of high relevance in the medical context. In this work, we compare three neural network architectures that combine feature representations on patch-level to a slide-level prediction with respect to their classification performance and evaluate their calibration. As slide-level classification task, we choose the prediction of Microsatellite Instability from Colorectal Cancer tissue sections. We observe that Transformers lead to good results in terms of classification performance and calibration. When evaluating the classification performance on a separate dataset, we observe that Transformers generalize best. The investigation of reliability diagrams provides additional insights to the Expected Calibration Error metric and we observe that especially Transformers push the output probabilities to extreme values, which results in overconfident predictions.
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