  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.
