TY - GEN
T1 - An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
AU - Shi, Xueying
AU - Dou, Qi
AU - Xue, Cheng
AU - Qin, Jing
AU - Chen, Hao
AU - Heng, Pheng Ann
N1 - Funding Information:
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.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85075663751
U2 - 10.1007/978-3-030-32692-0_72
DO - 10.1007/978-3-030-32692-0_72
M3 - Conference article published in proceeding or book
AN - SCOPUS:85075663751
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 628
EP - 636
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -
