In this paper, we describe our method for skin lesion classification. The goal is to classify skin lesions based on dermoscopic images to several diagnoses’ classes presented in the HAM (Human Against Machine) dataset: melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), and vascular lesion (VASC). We propose a simplified solution which has a better accuracy than previous methods, but only predicted on a single model that is practical for a real-world scenario. Our results show that using a network with additional metadata as input achieves a better classification performance. This metadata includes both the patient information and the extra information during the data augmentation process. On the international skin imaging collaboration (ISIC) 2018 skin lesion classification challenge test set, our algorithm yields a balanced multiclass accuracy of 88.7% on a single model and 89.5% for the embedding solution, which makes it the currently first ranked algorithm on the live leaderboard. To improve the inference accuracy. Test time augmentation (TTA) is applied. We also demonstrate how Grad-CAM is applied in TTA. Therefore, TTA and Grad-CAM can be integrated in heat map generation, which can be very helpful to assist the clinician for diagnosis.
Skin Lesion Classification With Deep CNN Ensembles
