In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To date, diagnosing diseases from images is one of the most explored applications of DL within dermatology. Convolutional neural networks (ConvNets) are the most common (DL) method in medical imaging due to their training efficiency and accuracy, although they are often described as black boxes because of their limited explainability. One popular way to obtain insight into a ConvNet's decision mechanism is gradient class activation maps (Grad-CAM). A quantitative evaluation of the Grad-CAM explainability has been recently made possible by the release of DermXDB, a skin disease diagnosis explainability dataset which enables explainability benchmarking of ConvNet architectures. In this paper, we perform a literature review to identify the most common ConvNet architectures used for this task, and compare their Grad-CAM explanations with the explanation maps provided by DermXDB. We identified 11 architectures: DenseNet121, EfficientNet-B0, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, NASNetMobile, ResNet50, ResNet50V2, VGG16, and Xception. We pre-trained all architectures on an clinical skin disease dataset, and fine-tuned them on a DermXDB subset. Validation results on the DermXDB holdout subset show an explainability F1 score of between 0.35-0.46, with Xception displaying the highest explainability performance. NASNetMobile reports the highest characteristic-level explainability sensitivity, despite it's mediocre diagnosis performance. These results highlight the importance of choosing the right architecture for the desired application and target market, underline need for additional explainability datasets, and further confirm the need for explainability benchmarking that relies on quantitative analyses.

翻译：近年来，得益于深度学习的成功，皮肤病学领域的机器学习方法取得了长足进步。迄今为止，从图像诊断疾病是深度学习在皮肤病学中应用最广泛的领域之一。卷积神经网络因训练效率高且准确率卓越，成为医学影像分析中最常见的深度学习方法，然而其有限的解释性常使其被形容为"黑箱"。梯度类激活映射是对卷积神经网络决策机制进行解析的常用方法。随着皮肤病诊断可解释性数据集DermXDB的发布，近期已可对Grad-CAM的可解释性进行定量评估，该数据集支持卷积神经网络架构的可解释性基准测试。本文通过文献综述识别该任务中最常用的11种卷积神经网络架构，并将它们的Grad-CAM解释结果与DermXDB提供的解释图谱进行对比。这11种架构包括：DenseNet121、EfficientNet-B0、InceptionV3、InceptionResNetV2、MobileNet、MobileNetV2、NASNetMobile、ResNet50、ResNet50V2、VGG16和Xception。我们在临床皮肤病数据集上对所有架构进行预训练，并在DermXDB子集上进行微调。在DermXDB保留子集上的验证结果显示，可解释性F1分数介于0.35-0.46之间，其中Xception表现出最高的可解释性性能。尽管NASNetMobile诊断性能平庸，但其特征层面可解释敏感性最高。这些结果凸显了针对目标应用和市场需求选择恰当架构的重要性，同时表明需要更多可解释性数据集，并进一步证实依赖定量分析的可解释性基准测试的必要性。