Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data. Especially in medical disciplines such as histopathology, this is crucial since large datasets with detailed annotations are scarce. While the majority of existing UDA methods focus on the adaptation from a labeled source to a single unlabeled target domain, many real-world applications with a long life cycle involve more than one target domain. Thus, the ability to sequentially adapt to multiple target domains becomes essential. In settings where the data from previously seen domains cannot be stored, e.g., due to data protection regulations, the above becomes a challenging continual learning problem. To this end, we propose to use generative feature-driven image replay in conjunction with a dual-purpose discriminator that not only enables the generation of images with realistic features for replay, but also promotes feature alignment during domain adaptation. We evaluate our approach extensively on a sequence of three histopathological datasets for tissue-type classification, achieving state-of-the-art results. We present detailed ablation experiments studying our proposed method components and demonstrate a possible use-case of our continual UDA method for an unsupervised patch-based segmentation task given high-resolution tissue images. Our code is available at: https://github.com/histocartography/multi-scale-feature-alignment.

中文翻译：

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用于未标记域持续学习的多尺度特征对齐


无监督域适应（UDA）方法有助于在没有任何标记数据的情况下提高深度神经网络在看不见的域上的性能。特别是在组织病理学等医学学科中，这一点至关重要，因为带有详细注释的大型数据集很少。虽然大多数现有 UDA 方法侧重于从标记源到单个未标记目标域的适应，但许多具有较长生命周期的实际应用程序涉及多个目标域。因此，顺序适应多个目标域的能力变得至关重要。在无法存储来自先前看到的域的数据的设置中，例如，由于数据保护法规，上述问题成为具有挑战性的持续学习问题。为此，我们建议将生成特征驱动的图像重放与双用途鉴别器结合使用，不仅能够生成具有真实特征的图像以供重放，而且还可以在域适应期间促进特征对齐。我们在用于组织类型分类的三个组织病理学数据集序列上广泛评估我们的方法，取得了最先进的结果。我们提出了详细的消融实验，研究我们提出的方法组件，并展示了我们的连续 UDA 方法的可能用例，用于给定高分辨率组织图像的无监督的基于块的分割任务。我们的代码位于：https://github.com/histocartography/multi-scale-feature-alignment。