Abstract
Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved remarkable performance on this challenging task, however, heavily relying on large-scale labelled datasets. In this paper, we present a novel active learning framework for cost-effective skin lesion analysis. The goal is to effectively select and utilize much fewer labelled samples, while the network can still achieve state-of-the-art performance. Our sample selection criteria complementarily consider both informativeness and representativeness, derived from decoupled aspects of measuring model certainty and covering sample diversity. To make wise use of the selected samples, we further design a simple yet effective strategy to aggregate intra-class images in pixel space, as a new form of data augmentation. We validate our proposed method on data of ISIC 2017 Skin Lesion Classification Challenge for two tasks. Using only up to 50% of samples, our approach can achieve state-of-the-art performances on both tasks, which are comparable or exceeding the accuracies with full-data training, and outperform other well-known active learning methods by a large margin.
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Acknowledgments
The work described in this paper was supported by the 973 Program with Project No. 2015CB351706, the National Natural Science Foundation of China with Project No. U1613219 and the Hong Kong Innovation and Technology Commission through the ITF ITSP Tier 2 Platform Scheme under Project ITS/426/17FP.
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Shi, X., Dou, Q., Xue, C., Qin, J., Chen, H., Heng, PA. (2019). An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_72
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DOI: https://doi.org/10.1007/978-3-030-32692-0_72
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