Unsupervised domain adaptation (UDA), aiming to adapt the model to an unseen domain without annotations, has drawn sustained attention in surgical instrument segmentation. Existing UDA methods neglect the domain-common knowledge of two datasets, thus failing to grasp the inter-category relationship in the target domain and leading to poor performance. To address these issues, we propose a graph-based unsupervised domain adaptation framework, named Interactive Graph Network (IGNet), to effectively adapt a model to an unlabeled new domain in surgical instrument segmentation tasks. In detail, the Domain-common Prototype Constructor (DPC) is first advanced to adaptively aggregate the feature map into domain-common prototypes using the probability mixture model, and construct a prototypical graph to interact the information among prototypes from the global perspective. In this way, DPC can grasp the co-occurrent and long-range relationship for both domains. To further narrow down the domain gap, we design a Domain-common Knowledge Incorporator (DKI) to guide the evolution of feature maps towards domain-common direction via a common-knowledge guidance graph and category-attentive graph reasoning. At last, the Cross-category Mismatch Estimator (CME) is developed to evaluate the category-level alignment from a graph perspective and assign each pixel with different adversarial weights, so as to refine the feature distribution alignment. The extensive experiments on three types of tasks demonstrate the feasibility and superiority of IGNet compared with other state-of-the-art methods. Furthermore, ablation studies verify the effectiveness of each component of IGNet. The source code is available at https://github.com/CityU-AIM-Group/Prototypical-Graph-DA .

中文翻译：

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基于领域常识的基于图形的手术器械自适应分割

无监督域适应 (UDA) 旨在使模型适应没有注释的看不见的域，在手术器械分割中引起了持续关注。现有的UDA方法忽略了两个数据集的领域公共知识，因此无法掌握目标领域的类别间关系，导致性能不佳。为了解决这些问题，我们提出了一个基于图的无监督域适应框架，称为交互式图网络（IGNet），以有效地使模型适应手术器械分割任务中未标记的新域。详细地说，领域公共原型构造器（DPC）首先被改进以使用概率混合模型将特征图自适应地聚合成领域公共原型，并构建一个原型图，从全局角度交互原型之间的信息。通过这种方式，DPC 可以掌握两个领域的同现和远程关系。为了进一步缩小领域差距，我们设计了一个领域共同知识整合器（DKI），通过共同知识指导图和类别注意图推理来指导特征图向领域共同方向的演变。最后，开发了跨类别不匹配估计器（CME），从图的角度评估类别级别的对齐，并为每个像素分配不同的对抗性权重，从而细化特征分布对齐。与其他最先进的方法相比，对三种任务的广泛实验证明了 IGNet 的可行性和优越性。此外，消融研究验证了 IGNet 的每个组件的有效性。源代码可在https://github.com/CityU-AIM-Group/Prototypical-Graph-DA .