Adaptive Query Prompting for Multi-Domain Landmark Detection
Qiusen Wei Guoheng Huang Xiaochen Yuan Xuhang Chen Guo Zhong Jianwen Huang Jiajie Huang
Medical landmark detection is crucial in various medical imaging modalities
and procedures. Although deep learning-based methods have achieve promising
performance, they are mostly designed for specific anatomical regions or tasks.
In this work, we propose a universal model for multi-domain landmark detection
by leveraging transformer architecture and developing a prompting component,
named as Adaptive Query Prompting (AQP). Instead of embedding additional
modules in the backbone network, we design a separate module to generate
prompts that can be effectively extended to any other transformer network. In
our proposed AQP, prompts are learnable parameters maintained in a memory space
called prompt pool. The central idea is to keep the backbone frozen and then
optimize prompts to instruct the model inference process. Furthermore, we
employ a lightweight decoder to decode landmarks from the extracted features,
namely Light-MLD. Thanks to the lightweight nature of the decoder and AQP, we
can handle multiple datasets by sharing the backbone encoder and then only
perform partial parameter tuning without incurring much additional cost. It has
the potential to be extended to more landmark detection tasks. We conduct
experiments on three widely used X-ray datasets for different medical landmark
detection tasks. Our proposed Light-MLD coupled with AQP achieves SOTA
performance on many metrics even without the use of elaborate structural
designs or complex frameworks.