Self-Paced Balance Learning for Clinical Skin Disease Recognition
文献类型：期刊论文
| 作者 | Yang, Jufeng2; Wu, Xiaoping2; Liang, Jie2; Sun, Xiaoxiao2; Cheng, Ming-Ming2; Rosin, Paul L.1; Wang, Liang3
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2020-08-01 |
| 卷号 | 31期号:8页码:2832-2846 |
| ISSN号 | 2162-237X |
| 关键词 | Diseases Skin Complexity theory Training Task analysis Image recognition Learning systems Class imbalance clinical skin disease recognition complexity level self-paced balance learning (SPBL) |
| DOI | 10.1109/TNNLS.2019.2917524 |
| 通讯作者 | Yang, Jufeng(yangjufeng@nankai.edu.cn) |
| 英文摘要 | Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less training samples than others. Most existing approaches aim to remedy the imbalanced number of instances among categories by resampling the majority and minority classes accordingly. However, the imbalanced level of difficulty of recognizing different categories is also crucial, especially for distinguishing samples with many classes. For example, in the task of clinical skin disease recognition, several rare diseases have a small number of training samples, but they are easy to diagnose because of their distinct visual properties. On the other hand, some common skin diseases, e.g., eczema, are hard to recognize due to the lack of special symptoms. To address this problem, we propose a self-paced balance learning (SPBL) algorithm in this paper. Specifically, we introduce a comprehensive metric termed the complexity of image category that is a combination of both sample number and recognition difficulty. First, the complexity is initialized using the model of the first pace, where the pace indicates one iteration in the self-paced learning paradigm. We then assign each class a penalty weight that is larger for more complex categories and smaller for easier ones, after which the curriculum is reconstructed by rearranging the training samples. Consequently, the model can iteratively learn discriminative representations via balancing the complexity in each pace. Experimental results on the SD-198 and SD-260 benchmark data sets demonstrate that the proposed SPBL algorithm performs favorably against the state-of-the-art methods. We also demonstrate the effectiveness of the SPBL algorithm's generalization capacity on various tasks, such as indoor scene image recognition and object classification. |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORKS ; SUPPORT VECTOR MACHINES ; CLASSIFICATION ; SMOTE ; RULE |
| 资助项目 | NSFC[61876094] ; Natural Science Foundation of Tianjin, China[18JCYBJC15400] ; Natural Science Foundation of Tianjin, China[18ZXZNGX00110] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Fundamental Research Funds for the Central Universities |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000557365700013 |
| 资助机构 | NSFC ; Natural Science Foundation of Tianjin, China ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/40352]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Yang, Jufeng |
| 作者单位 | 1.Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF10 3AT, Wales 2.Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Jufeng,Wu, Xiaoping,Liang, Jie,et al. Self-Paced Balance Learning for Clinical Skin Disease Recognition[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(8):2832-2846. |
| APA | Yang, Jufeng.,Wu, Xiaoping.,Liang, Jie.,Sun, Xiaoxiao.,Cheng, Ming-Ming.,...&Wang, Liang.(2020).Self-Paced Balance Learning for Clinical Skin Disease Recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(8),2832-2846. |
| MLA | Yang, Jufeng,et al."Self-Paced Balance Learning for Clinical Skin Disease Recognition".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.8(2020):2832-2846. |
入库方式： OAI收割
来源：自动化研究所
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