Clinical-Inspired Network for Skin Lesion Recognition

Abstract

Automated skin lesion recognition methods are useful for improving the diagnostic accuracy in dermoscopy images. However, several challenges delayed the pace of the development of these methods, including limited amount of data, a lack of ability to focus on the lesion area, poor performance for distinguishing between visually-similar categories of diseases and an imbalance between different classes of training data. During practical learning and diagnosis process, doctors conduct certain strategies to tackle these challenges. Thus, it’s really appealing to involve these strategies in automated skin lesion recognition method, which could be promising for a better performance. Inspired by this, we propose a new Clinical-Inspired Network (CIN) to simulate the subjective learning and diagnostic process of doctors. To mimic the diagnostic process, we design three modules, including a lesion area attention module to crop the images, a feature extraction module to extract image features and a lesion feature attention module to focus on the important lesion parts and mine the correlation between different lesion parts. To simulate the learning process, we introduce a distinguish module. The CIN is extensively tested on ISBI2016 and 2017 challenge datasets and achieves state-of-the-art performance, which demonstrates its advantages.