通道缩放：一种基于缩放选择的迁移学习方法
Channel Scaling: A Scale-and-Select Approach for Transfer Learning
摘要：
使用预训练的神经网络的迁移学习是用于在医学图像分析中训练分类器的常见策略。如果没有适当的通道选择，这通常会导致不必要的大模型，阻碍部署和解释。在本文中，我们提出了一种新的方法，通过引入信道缩放层来有效地构建小型和性能良好的网络。通道缩放层被附加到每个冻结卷积层，可训练的缩放权重推断相应特征通道的重要性。与微调方法不同，我们保持了原始通道的权重，并且不需要大型数据集。通过对缩放权重进行L1正则化和阈值化，该框架迭代地从预训练模型中删除不必要的特征通道。使用ImageNet预训练的VGG16模型，我们展示了所提出的框架对胸部X射线图像不透明度进行分类的能力。结果表明，我们可以减少95%的参数数量，同时提供卓越的性能。
英文摘要：
Transfer learning with pre-trained neural networks is a common strategy for training classifiers in medical image analysis. Without proper channel selections, this often results in unnecessarily large models that hinder deployment and explainability. In this paper, we propose a novel approach to efficiently build small and well performing networks by introducing the channel-scaling layers. A channel-scaling layer is attached to each frozen convolutional layer, with the trainable scaling weights inferring the importance of the corresponding feature channels. Unlike the fine-tuning approaches, we maintain the weights of the original channels and large datasets are not required. By imposing L1 regularization and thresholding on the scaling weights, this framework iteratively removes unnecessary feature channels from a pre-trained model. Using an ImageNet pre-trained VGG16 model, we demonstrate the capabilities of the proposed framework on classifying opacity from chest X-ray images. The results show that we can reduce the number of parameters by 95% while delivering a superior performance.
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。
