The popularity of large-scale pre-training has promoted the development of medical foundation models. However, some studies have shown that although foundation models exhibit strong general feature extraction capabilities, their performance on specific tasks is still inferior to task-specific methods. In this paper, we explore a new perspective called ``Knowledge Decomposition'' to improve the performance on specific medical tasks, which deconstruct the foundation model into multiple lightweight expert models, each dedicated to a particular task, with the goal of improving specialization while concurrently mitigating resource expenditure. To accomplish the above objective, we design a novel framework named Low-Rank Knowledge Decomposition (LoRKD), which explicitly separates graidents by incorporating low-rank expert modules and the efficient knowledge separation convolution. Extensive experimental results demonstrate that the decomposed models perform well in terms of performance and transferability, even surpassing the original foundation models.

本文通过知识分解的方法，设计了一种名为Low-Rank Knowledge Decomposition (LoRKD)的新型框架，将医学基础模型拆分为多个轻量级专家模型，以提高特定医学任务的性能和专业化，并在资源消耗上达到平衡。实验结果表明，拆分后的模型在性能和可迁移性方面表现良好，甚至超过原始的基础模型。

医学基础模型的低秩知识分解