论文标题
评估皮肤学工作流中深度学习方法的鲁棒性
Assessing Robustness of Deep learning Methods in Dermatological Workflow
论文作者
论文摘要
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，尤其是通过关注皮肤病学。尽管已经尝试在几种单个条件下获得皮肤科医生水平的准确性，但尚未对常见的临床抱怨进行严格的测试。大多数项目涉及在控制良好的实验室条件下获得的数据。这可能不能反映出相应图像质量并不总是理想的常规临床评估。 We test the robustness of deep learning methods by simulating non-ideal characteristics on user submitted images of ten classes of diseases.通过模仿条件进行评估，我们发现尽管训练良好，但在许多情况下，在许多情况下，降低的总体准确性也发生了很大变化。
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.
