ABO-Med：用于少样本医学图像分类的加速双层优化
ABO-Med: Accelerated Bilevel Optimization for Few-Shot Medical Image Classification
AI总结：
提出ABO-Med，一种基于一阶双层优化的少样本医学图像分类方法，结合MedRAug增强策略，在多个数据集上显著提升准确率并具有理论保证。
AI中文摘要：
近年来，双层优化已广泛应用于多种机器学习任务中。然而，先前的双层优化算法通常需要计算二阶信息，这限制了其实际可扩展性。直到最近，才建立了一种用于双层优化的一阶范式，在解决双层优化问题方面获得了接近最优的理论保证。在本文中，我们提出了ABO-Med，该方法是该范式在少样本学习中的可扩展实例化，通过将其整合到模型无关元学习（MAML）框架中，并针对医学图像分类进行定制。我们还引入了医学自适应随机增强（MedRAug），这是一种专为医学图像设计的模态感知增强策略。理论上，ABO-Med确立了MAML型元学习方法的优化性。实证上，ABO-Med在多个公开医学数据集上优于先前基线，增益为1.99%至18.76%，而MedRAug进一步将平均准确率提高了2.20%至6.34%。额外的跨域实验、增强消融研究、骨干网络消融研究以及训练效率分析进一步验证了所提出方法的有效性和效率。
英文摘要：
In recent years, bilevel optimization has been widely used in a variety of machine learning tasks. However, prior bilevel optimization algorithms generally require the computation of second-order information, which limits their practical scalability. Only recently has a first-order paradigm for bilevel optimization been established, attaining near-optimal theoretical guarantees for solving bilevel optimization problems. In this paper, we propose ABO-Med, a scalable instantiation of this paradigm for few-shot learning, by incorporating it into the model-agnostic meta-learning (MAML) framework and tailoring it to medical image classification. We also introduce Medical Adaptive RandomAugment (MedRAug), a modality-aware augmentation strategy designed for medical images. Theoretically, ABO-Med establishes the optimality of MAML-type meta-learning approaches. Empirically, ABO-Med outperforms prior baselines on several public medical datasets, with gains of 1.99% to 18.76%, while MedRAug further improves the average accuracy by 2.20% to 6.34%. Additional cross-domain experiments, augmentation ablation studies, backbone ablation studies, and training efficiency analysis further validate the effectiveness and efficiency of the proposed method.
