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：单样本医学标志点检测因其标注高效的训练过程而备受关注并取得显著成功。然而，现有单样本学习方法高度专精于单一领域，在多领域无标注数据场景下存在严重的领域偏好问题。此外，单样本学习鲁棒性不足，当标注次优图像时会出现性能下降。为解决这些问题，我们致力于开发一种面向多领域医学图像的领域自适应单样本标志点检测框架，命名为通用单样本检测（UOD）。UOD包含两个阶段及两个对应的通用模型，这些模型被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一种领域自适应卷积模型以生成伪标志点标签。在第二阶段，我们设计了一种领域自适应Transformer来消除领域偏好，并为多领域数据建立全局上下文关联。尽管每个领域仅有一个标注样本可用于训练，但领域共享模块帮助UOD聚合所有单样本样本，从而检测更鲁棒且更准确的标志点。我们针对三个广泛使用的不同解剖领域（即头部、手部、胸部）公开X光数据集，从定性和定量两方面对所提出的UOD进行了研究，并在每个领域均取得了最先进的性能。