Auxiliary Decoder and Classifier for Imbalanced Skin Disease Diagnosis

Abstract

To date, deep learning has been widely adopted in medical diagnosis systems and made great achievements in real-world applications. However, in medical image-based intelligent diagnosis, the phenomenon of class imbalance often appears due to the substantially smaller available training data for rare diseases compared to common diseases, which usually degrade the performance of classification dramatically. In this paper, we propose a novel learning framework to effectively alleviate the impact of class imbalance, adding an auxiliary decoder to reconstruct original images and reusing the original CNN classifier to help the classifier more likely extract disease-relevant features for both rare and common diseases. Throughout experiments on two skin disease datasets support that the proposed framework outperforms strong baselines with a very explicit margin. The proposed method is also independent of network architecture so that it can be flexibly combined with different model structures. Besides, applying our proposed method with existing training strategies designed for class imbalance can still improve the classification performance.