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.

提出了一种医疗图像领域自适应的单次学习里程碑检测框架（UOD），通过使用两个阶段的通用模型和领域特定模块，实现了多领域无标签数据的领域偏好适应，并在 X-ray 头，手和胸部三类不同解剖部位的公共数据集上获得了最先进的表现。