  Deep learning models for medical image analysis easily suffer from
distribution shifts caused by dataset artifacts bias, camera variations,
differences in the imaging station, etc., leading to unreliable diagnoses in
real-world clinical settings. Domain generalization (DG) methods, which aim to
train models on multiple domains to perform well on unseen domains, offer a
promising direction to solve the problem. However, existing DG methods assume
domain labels of each image are available and accurate, which is typically
feasible for only a limited number of medical datasets. To address these
challenges, we propose a novel DG framework for medical image classification
without relying on domain labels, called Prompt-driven Latent Domain
Generalization (PLDG). PLDG consists of unsupervised domain discovery and
prompt learning. This framework first discovers pseudo domain labels by
clustering the bias-associated style features, then leverages collaborative
domain prompts to guide a Vision Transformer to learn knowledge from discovered
diverse domains. To facilitate cross-domain knowledge learning between
different prompts, we introduce a domain prompt generator that enables
knowledge sharing between domain prompts and a shared prompt. A domain mixup
strategy is additionally employed for more flexible decision margins and
mitigates the risk of incorrect domain assignments. Extensive experiments on
three medical image classification tasks and one debiasing task demonstrate
that our method can achieve comparable or even superior performance than
conventional DG algorithms without relying on domain labels. Our code will be
publicly available upon the paper is accepted.
