标题： UNICON：医学基础模型的统一持续学习 显示英文标题

标题： UNICON: UNIfied CONtinual Learning for Medical Foundational Models

摘要： 基础模型在大规模数据集上进行训练，以捕捉领域的一般趋势。 然而，在医学影像领域，数据的稀缺性使得为每个领域、模态或任务进行预训练具有挑战性。 持续学习通过在不同领域或任务上依次微调模型来提供解决方案，使模型能够在不为每个训练阶段提供大量数据的情况下整合新知识。 在本文中，我们提出了UNIfied CONtinual Learning for Medical Foundational Models (UNICON)，这是一种使基础模型能够无缝适应多种领域、任务和模态的框架。 与传统适应方法将这些变化单独处理不同，UNICON提供了一个统一且可不断扩展的框架。 通过精心整合，我们证明基础模型可以动态地扩展到不同的成像模态、解剖区域和临床目标，而不会出现灾难性遗忘或任务干扰。 实证上，我们通过将最初用于分类的胸部CT基础模型适应到预后和分割任务来验证我们的方法。 我们的结果表明，在额外任务上性能均有提升。 此外，我们持续引入了PET扫描，并在Dice分数上相比各自基线提升了5%。 这些发现表明，基础模型并不固有地受限于其初始训练范围，而是可以进化，为医学影像的通用人工智能模型铺平了道路。 显示英文摘要

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.