UNICON：面向医学基础模型的统一持续学习框架
UNICON: UNIfied CONtinual Learning for Medical Foundational Models
AI总结：
本文提出UNICON统一持续学习框架，使医学基础模型能够跨模态、解剖区域和临床任务动态扩展，在CT预后与分割任务及PET扫描中验证了性能提升，避免了灾难性遗忘。
摘要：
基础模型在大规模数据集上训练，以捕捉某个领域的总体趋势。然而，在医学影像领域，数据稀缺使得针对每个领域、模态或任务进行预训练变得困难。持续学习提供了一种解决方案，通过在不同领域或任务上顺序微调模型，使其能够整合新知识，而无需为每个训练阶段准备大规模数据集。本文提出了面向医学基础模型的统一持续学习框架（UNICON），该框架能够使基础模型无缝适应多样化的领域、任务和模态。与将这些变化孤立处理的传统适应方法不同，UNICON提供了一个统一的、可永久扩展的框架。通过精心设计的信息整合，我们证明基础模型能够在影像模态、解剖区域和临床目标之间动态扩展，而不会出现灾难性遗忘或任务干扰。在实验方面，我们通过将一个最初为分类任务训练的胸部CT基础模型适配到预后预测和分割任务来验证所提方法。结果表明，在这两个新增任务上性能均有所提升。此外，我们持续引入PET扫描数据，与相应基线相比，Dice分数提高了5%。这些发现表明，基础模型并非固有地受限于其初始训练范围，而是能够持续演化，为面向医学影像的通用人工智能模型铺平了道路。
英文摘要：
Foundational models are trained on extensive datasets to capture the general trends of a domain. However, in medical imaging, the scarcity of data makes pre-training for every domain, modality, or task challenging. Continual learning offers a solution by fine-tuning a model sequentially on different domains or tasks, enabling it to integrate new knowledge without requiring large datasets for each training phase. In this paper, we propose UNIfied CONtinual Learning for Medical Foundational Models (UNICON), a framework that enables the seamless adaptation of foundation models to diverse domains, tasks, and modalities. Unlike conventional adaptation methods that treat these changes in isolation, UNICON provides a unified, perpetually expandable framework. Through careful integration, we show that foundation models can dynamically expand across imaging modalities, anatomical regions, and clinical objectives without catastrophic forgetting or task interference. Empirically, we validate our approach by adapting a chest CT foundation model initially trained for classification to a prognosis and segmentation task. Our results show improved performance across both additional tasks. Furthermore, we continually incorporated PET scans and achieved a 5\% improvement in Dice score compared to respective baselines. These findings establish that foundation models are not inherently constrained to their initial training scope but can evolve, paving the way toward generalist AI models for medical imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
