One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data. Moreover, one-shot learning is not robust that it faces performance drop when annotating a sub-optimal image. To tackle these issues, we resort to developing a domain-adaptive one-shot landmark detection framework for handling multi-domain medical images, named Universal One-shot Detection (UOD). UOD consists of two stages and two corresponding universal models which are designed as combinations of domain-specific modules and domain-shared modules. In the first stage, a domain-adaptive convolution model is self-supervised learned to generate pseudo landmark labels. In the second stage, we design a domain-adaptive transformer to eliminate domain preference and build the global context for multi-domain data. Even though only one annotated sample from each domain is available for training, the domain-shared modules help UOD aggregate all one-shot samples to detect more robust and accurate landmarks. We investigated both qualitatively and quantitatively the proposed UOD on three widely-used public X-ray datasets in different anatomical domains (i.e., head, hand, chest) and obtained state-of-the-art performances in each domain.

翻译：摘要：一次性医学地标检测因其标签高效的训练过程而受到广泛关注并取得了显著成功。然而，现有的单次学习方法高度专精于单一领域，在多域无标签数据情境下严重受限于领域偏好。此外，一次性学习鲁棒性不足，当标注次优图像时会出现性能下降。为解决这些问题，我们提出开发一种领域自适应的一次性地标检测框架，用于处理多域医学图像，命名为通用一次性检测（UOD）。UOD 包含两个阶段及两个对应的通用模型，这些模型设计为领域特定模块与领域共享模块的组合。在第一阶段，通过自监督学习训练领域自适应卷积模型以生成伪地标标签。在第二阶段，我们设计领域自适应变换器以消除领域偏好并构建多域数据的全局上下文。尽管每个领域仅有一个标注样本可用于训练，领域共享模块帮助 UOD 聚合所有一次性样本以检测更鲁棒且准确的地标。我们在三个广泛使用的公开X射线数据集（分别对应头部、手部、胸部等不同解剖领域）上对提出的 UOD 进行了定性和定量研究，并在每个领域获得了最先进的性能。