Integrating Label-Guided Decision Modeling with Cross-Modal Statistical Alignment for Medical Multimodal Class-Incremental Learning

Medical multimodal incremental learning aims to identify emerging diseases or subtypes while retaining knowledge of previously learned conditions to meet evolving diagnostic needs. Existing methods typically map multimodal information into a shared representation space and select classes based on similarity relationships among the extracted features. However, merely maintaining cross-modal alignment fails to model the discriminative space required for downstream classification tasks, making it difficult to effectively distinguish between visually similar categories. To address this, we propose a continual statistical learning framework based on frozen visual-language representations. We first introduce dimensional uncertainty modeling ($U^t$), which utilizes class-conditional statistical information to dynamically calibrate the reliability of visual features, providing a more reliable basis for subsequent cross-modal statistical organization. Building on this calibrated organization, we then address the more fundamental alignment-classification mismatch through a Label-Guided Decision Module, which integrates statistics linking features to class labels to recover missing category information and transforms organized representations into discriminative decision structures. By leveraging accumulated sufficient statistics, the framework achieves replay-free continual adaptation without requiring access to historical samples or further optimization of the pre-trained encoder. Experiments across six medical continual learning protocols demonstrate the dynamic evolution of feature reliability and sustained improvements in classification performance. Notably, decision geometry improvements far exceed gains in mean class recall (MCR), which averages recall equally across classes. This further underscores the necessity of employing label-conditioned decision modeling alongside cross-modal alignment.