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. The code is available at https://github.com/heqin-zhu/UOD_universal_oneshot_detection.

翻译：摘要：单次医学地标检测因其标注高效的训练过程而备受关注并取得了显著成功。然而，现有单次学习方法高度专用于单一领域，在处理多领域未标注数据时存在严重的领域偏好问题。此外，单次学习缺乏鲁棒性，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领域自适应的单次地标检测框架，用于处理多领域医学图像，命名为通用单次检测方法（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专属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生成伪地标标签。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，并为多领域数据构建全局上下文信息。尽管每个领域仅有一个标注样本可用于训练，领域共享模块仍能帮助UOD聚合所有单次样本，从而检测出更鲁棒、更准确的地标。我们在三个广泛使用的公开X射线数据集（涵盖头、手、胸等不同解剖领域）上对提出的UOD方法进行了定性与定量评估，并在每个领域均取得了最先进的性能。代码已开源：https://github.com/heqin-zhu/UOD_universal_oneshot_detection。