Context-guided Continual Reinforcement Learning for Landmark Detection with Incomplete Data

Existing landmark detection methods are primarily designed for centralized learning scenarios where all training data and labels are complete and available throughout the entire training phase. In real-world scenarios, training data may be collected sequentially, covering only part of the region of interest or providing incomplete landmark labels. In this work, we propose a novel continual reinforcement learning framework to tackle this complex situation in landmark detection. To handle the increasing number of landmark targets during training, we introduce a Q-learning network that takes both observations and prompts as input. The prompts are stored in a buffer and utilized to guide the prediction for each landmark, enabling our method to adapt to the intricacies of the data collection process. We validate our approach on two datasets: the RSNA-PBA dataset, representing scenarios with complete images and incomplete labels, and the WB-DXA dataset, representing situations where both images and labels are incomplete. The results demonstrate the effectiveness of the proposed method in landmark detection tasks with complex data structures. The source code will be available from https://github.com/kevinwolcano/CgCRL.