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

Low-Rank Knowledge Decomposition for Medical Foundation Models

Recently, we have witnessed impressive achievements in cancer survival
analysis by integrating multimodal data, e.g., pathology images and genomic
profiles. However, the heterogeneity and high dimensionality of these
modalities pose significant challenges for extracting discriminative
representations while maintaining good generalization. In this paper, we
propose a Cohort-individual Cooperative Learning (CCL) framework to advance
cancer survival analysis by collaborating knowledge decomposition and cohort
guidance. Specifically, first, we propose a Multimodal Knowledge Decomposition
(MKD) module to explicitly decompose multimodal knowledge into four distinct
components: redundancy, synergy and uniqueness of the two modalities. Such a
comprehensive decomposition can enlighten the models to perceive easily
overlooked yet important information, facilitating an effective multimodal
fusion. Second, we propose a Cohort Guidance Modeling (CGM) to mitigate the
risk of overfitting task-irrelevant information. It can promote a more
comprehensive and robust understanding of the underlying multimodal data, while
avoiding the pitfalls of overfitting and enhancing the generalization ability
of the model. By cooperating the knowledge decomposition and cohort guidance
methods, we develop a robust multimodal survival analysis model with enhanced
discrimination and generalization abilities. Extensive experimental results on
five cancer datasets demonstrate the effectiveness of our model in integrating
multimodal data for survival analysis.

我们提出了一种合作学习框架，通过知识分解和队列引导方法协同推进癌症生存分析，以提高多模态数据的融合、鉴别和泛化能力。通过四个不同组成部分：冗余性、协同性、独特性和队列引导建模，我们的模型在融合多模态数据进行生存分析时展现出了卓越的效果。