Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification. We study different alternatives of CNNs and Transforms as the teacher to distill the knowledge to a smaller student. Then, we employed explainable artificial intelligence (XAI) to provide the visual explanation for the model decision improved by the KD. Our results on three benchmark CXR datasets show that our KD strategy provides the improved performance on the compact student model, thus being the feasible choice for many limited hardware platforms. For instance, when using DenseNet161 as the teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer parameters of 4.7 million and computational cost of 0.3 billion FLOPS.

翻译：自动化多标签胸部X光图像分类通过利用复杂的深度学习方法已在临床诊断中取得了显著进展。然而，大多数深度模型计算需求高，使其难以适用于计算需求低的紧凑型设备。为解决此问题，我们提出了一种知识蒸馏策略，用于创建紧凑型深度学习模型，以实现实时多标签胸部X光图像分类。我们研究了不同结构的CNN和Transformer作为教师模型，将知识蒸馏至更小的学生模型。随后，我们采用可解释人工智能来提供模型决策的可视化解释，该决策通过知识蒸馏得以改进。在三个基准胸部X光数据集上的结果表明，我们的知识蒸馏策略在紧凑型学生模型上实现了性能提升，因此成为许多受限硬件平台的可行选择。例如，当使用DenseNet161作为教师网络时，EEEANet-C2在ChestXray14、CheXpert和PadChest数据集上分别实现了83.7%、87.1%和88.7%的AUC，且参数数量仅为470万，计算成本为3亿次浮点运算。