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：未受监督的域适应方法(UDA)有助于改善在没有标签数据的情况下在隐蔽域的深神经网络的性能,特别是在诸如组织病理学等医学学科中,这一点至关重要,因为具有详细说明的大型数据集很少。虽然现有的UDA方法大多侧重于从标签源改成单一未标签目标域的适应,但许多长寿命周期长的真实世界应用都涉及不止一个目标域。因此,根据组织类型分类、实现状态结果的三个直观数据集顺序,我们广泛评价我们的方法。在以前所见域的数据无法储存的环境中,例如,由于数据保护条例,上述数据成为一个具有挑战性的不断学习问题。为此,我们提议使用基因特性驱动的图像重播与双重目的歧视器一起,不仅能够生成具有现实功能重播功能的图像,而且还促进域适应期间的特征调整。我们广泛评价我们的方法,按照组织类型分类的三个直系病态数据集进行,实现状态结果。我们提出详细的不精确的实验,研究我们拟议的方法组成部分,并展示我们所给定的高分辨率任务段。