What evidence does deep learning model use to classify Skin Lesions?

Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions, especially in the early stage, is extremely challenging via dermatologists' observation. Hence, the discovery of reliable biomarkers for melanoma diagnosis will be meaningful. Recent years, deep learning empowered computer-assisted diagnosis has been shown its value in medical imaging-based decision making. However, lots of research focus on improving disease detection accuracy but not exploring the evidence of pathology. In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesion. Secondly, we utilize a prediction difference analysis method that examining each patch on the image through patch wised corrupting for detecting the biomarkers. Lastly, we validate that our biomarker findings are corresponding to the patterns in the literature. The findings might be significant to guide clinical diagnosis.