Abstract
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.
Abstract (translated)
近年来,在开发针对皮肤应用机器学习方法方面取得了巨大的进展,部分原因是深度学习的成功。目前,从图像诊断疾病是深度学习在皮肤学领域最探索的应用之一。卷积神经网络(ConvNets)是医学成像中最常见的(深度学习)方法,由于其训练效率和精度,尽管它们往往被称为黑盒子,因为它们 limited 的解释性。一种流行的方法是为了了解 ConvNet 的决策机制,是梯度类激活图(Grad-CAM)的量化评估。最近,由 DermXDB 发布的皮肤疾病诊断解释性数据集使得 ConvNet 架构的解释性基准项得以实现。在本文中,我们进行文献综述,以确定用于该任务最常见的 ConvNet 架构,并比较它们的grad-CAM解释与 DermXDB 提供的解释图。我们确定了11种架构:DenseNet121、EfficientNet-B0、InceptionV3、InceptionResNetV2、MobileNet、MobileNetV2、NASNetMobile、ResNet50、ResNet50V2、VGG16 和 Xception。我们首先在临床皮肤疾病数据集上预训练所有架构,并在 DermXDB 子集上微调。在 DermXDB 保留子集上的验证结果显示,解释性 F1 得分在0.35-0.46之间,Xception 表现出最高的解释性能。NASNetMobile 报告最高的特征级解释敏感性,尽管其诊断性能平庸。这些结果强调选择适合所需应用和目标市场的正确的架构的重要性,强调需要更多的解释性数据集,并进一步确认基于量化分析的解释性基准的必要性。
URL
https://arxiv.org/abs/2302.12084
