The high capacity of neural networks allows fitting models to data with high precision, but makes generalization to unseen data a challenge. If a domain shift exists, i.e. differences in image statistics between training and test data, care needs to be taken to ensure reliable deployment in real-world scenarios. In digital pathology, domain shift can be manifested in differences between whole-slide images, introduced by for example differences in acquisition pipeline – between medical centers or over time. In order to harness the great potential presented by deep learning in histopathology, and ensure consistent model behavior, we need a deeper understanding of domain shift and its consequences, such that a model's predictions on new data can be trusted. This work focuses on the internal representation learned by trained convolutional neural networks, and shows how this can be used to formulate a novel measure – the representation shift – for quantifying the magnitude of model-specific domain shift. We perform a study on domain shift in tumor classification of hematoxylin and eosin stained images, by considering different datasets, models, and techniques for preparing data in order to reduce the domain shift. The results show how the proposed measure has a high correlation with drop in performance when testing a model across a large number of different types of domain shifts, and how it improves on existing techniques for measuring data shift and uncertainty. The proposed measure can reveal how sensitive a model is to domain variations, and can be used to detect new data that a model will have problems generalizing to. We see techniques for measuring, understanding and overcoming the domain shift as a crucial step towards reliable use of deep learning in the future clinical pathology applications.

中文翻译：

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在组织病理学中测量深度学习的域转移

神经网络的高容量允许以高精度将模型拟合到数据，但使对看不见的数据的泛化成为一个挑战。如果一个领域转移存在，即训练和测试数据之间的图像统计差异，需要注意确保在现实世界场景中的可靠部署。在数字病理学中，域转移可以表现为整个幻灯片图像之间的差异，例如由采集管道的差异引入 - 医疗中心之间或随着时间的推移。为了利用深度学习在组织病理学中的巨大潜力，并确保一致的模型行为，我们需要更深入地了解域转移及其后果，以便可以信任模型对新数据的预测。这项工作侧重于训练有素的卷积神经网络学习的内部表示，并展示了如何使用它来制定新的度量——代表转移– 用于量化特定于模型的域偏移的大小。我们通过考虑不同的数据集、模型和准备数据的技术，对苏木精和伊红染色图像的肿瘤分类中的域偏移进行研究，以减少域偏移。结果表明，当在大量不同类型的域转移中测试模型时，所提出的度量如何与性能下降具有高度相关性，以及它如何改进现有的测量数据偏移和不确定性的技术。所提出的度量可以揭示模型对域变化的敏感程度，并可用于检测模型在泛化时存在问题的新数据。我们看到测量技术，