Prompt-driven Latent Domain Generalization for Medical Image Classification

TL;DR

This paper introduces PLDG, a novel domain generalization framework for medical image classification that discovers pseudo domains without labels, using unsupervised clustering and prompt learning to improve robustness across unseen domains.

Contribution

PLDG is the first to combine unsupervised domain discovery with prompt learning for medical image classification without relying on domain labels.

Findings

Achieves comparable or superior performance to existing DG methods.

Effectively discovers pseudo domains via style feature clustering.

Enhances cross-domain knowledge sharing with prompt mechanisms.

Abstract

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.