COA-Tuning: Collaborative Adapter Tuning with Prompt Enhancement for Class-Incremental Medical Image Learning

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Abstract
Continual learning plays a crucial role in enabling artificial intelligence systems to accumulate knowledge over time without overwriting previously acquired information. While this capability is essential for real-world applications, especially in clinical domains where data evolves continuously, most existing approaches are designed for natural image scenarios and struggle when applied to medical imaging tasks. Medical images exhibit high intra-class similarity, making fine-grained recognition difficult. Meanwhile, adapter-based tuning with pre-trained Vision Transformers introduces task-agnostic inference challenges due to the lack of task identity at test time. To overcome these obstacles, we propose a continual learning framework tailored for medical imaging, which enhances training and inference in class-incremental settings. First, we introduce a prompt-guided adapter design that focuses the model's representation capacity on subtle disease-specific differences, improving feature discrimination. Second, we encourage knowledge reuse by facilitating communication between task-specific adapters, allowing the model to build upon prior information. Finally, during testing, we employ a collaborative inference mechanism that dynamically integrates multiple adapters, enabling robust predictions without relying on task labels. Experiments on several public medical image datasets demonstrate that our approach achieves superior performance and substantially alleviates forgetting compared to existing methods.

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