Lu Liangfu, Cui Xudong, Tan Zhiyuan, Wu Yulei
IEEE/ACM Trans Comput Biol Bioinform. 2024 Jul-Aug;21(4):725-736. doi: 10.1109/TCBB.2023.3284846. Epub 2024 Aug 8.
In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area. This paper proposes a meta-learning framework, termed MedOptNet, for few-shot medical image classification. The framework enables the use of various high-performance convex optimization models as classifiers, such as multi-class kernel support vector machines, ridge regression, and other models. End-to-end training is then implemented using dual problems and differentiation in the paper. Additionally, various regularization techniques are employed to enhance the model's generalization capabilities. Experiments on the BreakHis, ISIC2018, and Pap smear medical few-shot datasets demonstrate that the MedOptNet framework outperforms benchmark models. Moreover, the model training time is also compared to prove its effectiveness in the paper, and an ablation study is conducted to validate the effectiveness of each module.
在医学研究领域，数据有限且标注成本高昂，使得少样本条件下的高效分类成为一个热门研究领域。本文提出了一种用于少样本医学图像分类的元学习框架，称为MedOptNet。该框架能够使用各种高性能凸优化模型作为分类器，如多类核支持向量机、岭回归等模型。然后，本文利用对偶问题和微分实现了端到端训练。此外，还采用了各种正则化技术来增强模型的泛化能力。在BreakHis、ISIC2018和巴氏涂片医学少样本数据集上的实验表明，MedOptNet框架优于基准模型。此外，本文还比较了模型训练时间以证明其有效性，并进行了消融研究以验证每个模块的有效性。
