通过结构化输出正则化，让小样本医学图像分类模型更精准适应特定数据。
Structured Output Regularization: a framework for few-shot transfer learning
- 冻结网络结构，用组Lasso和L1正则化实现轻量级适配
- 在三个少样本医疗图像任务上表现接近主流基准
- 适合资源有限的医疗影像场景，易部署于各类神经网络模块
传统迁移学习通常通过冻结部分权重并添加任务专用层来复用大型预训练网络。尽管计算高效，但限制了模型对领域特异性特征的适应能力，且在极少量数据下仍易过拟合。为此，我们提出结构化输出正则化（SOR）框架：冻结内部网络结构（如卷积滤波器），同时结合组Lasso与$L_1$惩罚。该方法以极少新增参数即可定制模型以适应特定数据，可灵活应用于卷积滤波器或神经网络中的各类模块，适用性广。我们在三个少样本医学图像分类任务上评估了SOR，使用DenseNet121和EfficientNetB4作为基础模型，结果与现有基准相当。
原文摘要 · Abstract (English)
Traditional transfer learning typically reuses large pre-trained networks by freezing some of their weights and adding task-specific layers. While this approach is computationally efficient, it limits the model's ability to adapt to domain-specific features and can still lead to overfitting with very limited data. To address these limitations, we propose Structured Output Regularization (SOR), a simple yet effective framework that freezes the internal network structures (e.g., convolutional filters) while using a combination of group lasso and $L_1$ penalties. This framework tailors the model to specific data with minimal additional parameters and is easily applicable to various network components, such as convolutional filters or various blocks in neural networks enabling broad applicability for transfer learning tasks. We evaluate SOR on three few shot medical imaging classification tasks and we achieve competitive results using DenseNet121, and EfficientNetB4 bases compared to established benchmarks.
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