Navigating the Unknown: A Novel MGUAN Framework for Medical Image Recognition Across Dynamic Domains

Machine learning has significantly advanced medical image recognition, enhancing diagnostic accuracy in various applications. However, these advancements primarily apply to scenarios with consistent data distributions, a condition rarely met in real-world clinical settings. In real-world clinical environments, variations in device specifications and patient demographics introduce distribution shifts, class imbalance and unknown class challenges, undermining model robustness. Addressing this, we present the Medical image recognition under Generalized Universal Domain Adaptation (MGUDA) concept, targeting distribution shifts, class imbalance and unknown class detection. Our innovative Medical Dual-Prototype Adaptation Network (MDPAN) framework, integrating dual prototype learning, dual prototype employment, and weighted multi-class adversarial alignment, adeptly confronts these issues. Extensive evaluations on diverse medical image datasets validate MDPAN’s superiority in managing class imbalances and enhancing target domain classification, marking a pivotal step in robust medical image recognition across variable domains.