This paper aims to evaluate the suitability of current deep learning methods for clinical workflow especially by focusing on dermatology. Although deep learning methods have been attempted to get dermatologist level accuracy in several individual conditions, it has not been rigorously tested for common clinical complaints. Most projects involve data acquired in well-controlled laboratory conditions. This may not reflect regular clinical evaluation where corresponding image quality is not always ideal. We test the robustness of deep learning methods by simulating non-ideal characteristics on user submitted images of ten classes of diseases. Assessing via imitated conditions, we have found the overall accuracy to drop and individual predictions change significantly in many cases despite of robust training.

中文翻译：

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评估皮肤病学工作流程中深度学习方法的稳健性

本文旨在评估当前深度学习方法对临床工作流程的适用性，特别是通过关注皮肤病学。尽管深度学习方法已尝试在几种个别情况下获得皮肤科医生级别的准确性，但尚未针对常见的临床症状进行严格测试。大多数项目涉及在控制良好的实验室条件下获得的数据。这可能无法反映相应的图像质量并不总是理想的常规临床评估。我们通过在用户提交的十类疾病图像上模拟非理想特征来测试深度学习方法的鲁棒性。通过模拟条件进行评估，我们发现尽管进行了稳健的训练，但在许多情况下，总体准确度下降和个体预测发生了显着变化。