Decoupling multi-task causality for improved skin lesion segmentation and classification

Abstract

Multi-task learning has been used widely in many computer aided diagnosis applications recently, while the trade-off between different tasks remains challenging. Also, the inherent causality is less studied. In this paper, we focus on skin lesion analysis, including lesion classification, detection and segmentation. By defining the chain relationship (i.e., lesion detection boosts contour segmentation, and segmentation boosts lesion classification in turn), and further decoupling each pair-wise causality (e.g., detection to segmentation) from the Pareto efficiency view, we can solve the common trade-off issue between multi-task. On this basis, we propose a novel paradigm to improve the skin lesion segmentation and classification separately, and favourable feature fusion ways for each task are explored. Moreover, to address the huge model size problem, we design an effective model compression scheme (MCS). Extensive experiments on the ISIC2017 and PH2 datasets are conducted to evaluate the proposed paradigm. The results demonstrate that the popular models such as ResNet, DenseNet and UNet for lesion analysis can be boosted by applying the proposed paradigm, and the designed MCS reduces the amount of model parameters efficiently. We achieve performance improvements on skin lesion segmentation and classification without strenuous network design and soaring model complexity. This proposed approach is promising for the multi-task diagnosis setting in other medical applications.