Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2660-2663. doi: 10.1109/EMBC46164.2021.9630047.
In this work, we compare the performance of six state-of-the-art deep neural networks in classification tasks when using only image features, to when these are combined with patient metadata. We utilise transfer learning from networks pretrained on ImageNet to extract image features from the ISIC HAM10000 dataset prior to classification. Using several classification performance metrics, we evaluate the effects of including metadata with the image features. Furthermore, we repeat our experiments with data augmentation. Our results show an overall enhancement in performance of each network as assessed by all metrics, only noting degradation in a vgg16 architecture. Our results indicate that this performance enhancement may be a general property of deep networks and should be explored in other areas. Moreover, these improvements come at a negligible additional cost in computation time, and therefore are a practical method for other applications.
在这项工作中，我们比较了在仅使用图像特征和将这些特征与患者元数据结合使用时，六种最先进的深度神经网络在分类任务中的性能。我们利用从预训练在 ImageNet 上的网络进行迁移学习，从 ISIC HAM10000 数据集提取图像特征，然后进行分类。使用几种分类性能指标，我们评估了将元数据与图像特征结合使用的效果。此外，我们还使用数据增强重复了实验。我们的结果表明，所有指标评估的每个网络的性能都得到了整体提高，仅注意到 vgg16 架构的性能下降。我们的结果表明，这种性能提升可能是深度网络的普遍特性，应该在其他领域进行探索。此外，这些改进在计算时间上增加的额外成本可以忽略不计，因此是其他应用的一种实用方法。
