The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data; in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition.

翻译：通过脱温图像自动检测皮肤疾病可以提高诊断效率,并有助于医生做出更准确的判断。然而,常规皮肤疾病识别系统可能会对外分配数据产生高度信心,这可能成为实际应用中的主要安全脆弱性。在本文中,我们提议了一个多尺度检测框架,以检测外分配皮肤疾病图像数据,确保系统的稳健性。我们的框架从神经网络的不同层面提取特征。在早期,纠正激活被用来使输出特征更接近于良好分布,然后对单级 SVM 进行检测 OOD数据的训练;在倒数层,采用经调整的Gram 矩阵来计算恢复激活后的特点,最后选择了最佳性能的层来计算正常性分数。实验表明,与皮肤疾病识别任务中的其他最先进的方法相比,拟议框架取得了优异的性能。