End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification

Due to its increasing incidence, skin cancer, and especially melanoma, is considered a major public health issue. Manually detecting skin lesions (SL) from dermoscopy images is a difficult and time-consuming process. Thus, researchers designed computer-aided diagnosis (CAD) systems to assist dermatologists in the early detection of skin cancer. Moreover, SL detection naturally exhibits a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Very limited research for handling this issue exists on SL detection. In this paper, we propose an end-to-end decoupled training for the long-tailed skin lesion classification task. Specifically, we initialized the training of a network with a novel loss function Lf able to guide the model to a better representation of the features. Then, we fine-tuned the pretrained networks with a weighted variant of Lf helping to improve the robustness of the network to class imbalance. We evaluated our model on the ISIC 2018 public dataset against existing methods for handling class imbalance and existing approaches for SL detection. The results demonstrated the superiority of our framework, outperforming all compared methods by a minimum margin of 2% with a single model.