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.

翻译：无监督域适应（UDA）方法有助于在无标注数据条件下提升深度神经网络对未知领域的性能表现。在组织病理学等医学学科中，这一点尤为关键，因为带有详细标注的大规模数据集十分稀缺。虽然现有UDA方法主要关注从带标签源域到单个无标签目标域的适应，但在许多具有长生命周期的实际应用中，往往涉及多个目标域。因此，顺序适应多个目标域的能力变得至关重要。当由于数据保护法规等原因无法存储先前已见领域的数据时，上述情况将演变为极具挑战性的持续学习问题。为此，我们提出将生成式特征驱动图像回放与双用途判别器相结合，该判别器不仅能生成具有真实特征的回放图像，还能在领域适应过程中促进特征对齐。我们在三个连续的组织病理学数据集上对组织类型分类任务进行了系统评估，取得了最先进的性能。通过详细的消融实验研究各方法组件的贡献，并展示了所提出的持续UDA方法在高分辨率组织图像无监督块级分割任务中的潜在应用场景。