Inferring Skin Lesion Segmentation With Fully Connected CRFs Based on Multiple Deep Convolutional Neural Networks

doi:10.1109/ACCESS.2020.3014787

	Inferring Skin Lesion Segmentation With Fully Connected CRFs Based on Multiple Deep Convolutional Neural Networks
	Qiu, Yuming 1,2,4; Cai, Jingyong 3; Qin, Xiaolin 1,2; Zhang, Ju 4
刊名	IEEE ACCESS
	2020
卷号	8 页码:144246-144258
关键词	Lesions Image segmentation Skin Measurement Predictive models Training Task analysis Deep convolutional neural networks ensemble learning fully connected CRFs pigmented skin lesion segmentation
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.3014787
通讯作者	Qiu, Yuming(ccqiuym@126.com)
英文摘要	This article presents a method to infer skin lesion segmentation based on multiple deep convolutional neural network (DCNN) models by employing fully connected conditional random fields (CRFs). This method is on the strength of the synergism between ensemble learning which is responsible for introducing diversity from multiple DCNN models and CRFs inference which is in charge of probabilistic inference based on random fields over dermoscopy images. Contrasting to single DCNN models, the proposed method can gain better segmentation by comprehensively utilizing the advances and performance preferences of multiple different DCNN models. In comparison with simple ensemble schemes, it can effectively and precisely refine the fuzzy lesion boundary by utilizing the information in test images to maximize label agreement between similar pixels. Further, an engineering bonus is the feasibility of parallelization for the heavy operation, predicting on multiple DCNN models. In experiments, we tested the effectiveness and robustness of the proposed method on the mainstream datasets ISIC 2017 and PH2, and the results were competitive with the state-of-art methods. we also confirmed that the proposed method can capture the local information in fuzzy dermoscopy images being able to find more accurate lesion borders with a good boost on Boundary Recall (BR) metric. Moreover, since the hyper-parameters in CRFs are explainable, it is possible to adjust them manually to reach better results case by case, being attractive in practice. This work is of value on integration between the deep learning technologies and probabilistic inference in resolving lesion segmentation, and has great potential to be applied in similar tasks.
资助项目	Sichuan Science and Technology Program[2018GZDZX0041] ; Sichuan Science and Technology Program[2019ZDZX0005] ; Sichuan Science and Technology Program[2019ZDZX0006]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000560364300001
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/11614]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Qiu, Yuming
作者单位	1.Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu 610041, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Tokyo Univ Agr & Technol, Inst Engn, Tokyo 1848588, Japan 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400010, Peoples R China
推荐引用方式 GB/T 7714	Qiu, Yuming,Cai, Jingyong,Qin, Xiaolin,et al. Inferring Skin Lesion Segmentation With Fully Connected CRFs Based on Multiple Deep Convolutional Neural Networks[J]. IEEE ACCESS,2020,8:144246-144258.
APA	Qiu, Yuming,Cai, Jingyong,Qin, Xiaolin,&Zhang, Ju.(2020).Inferring Skin Lesion Segmentation With Fully Connected CRFs Based on Multiple Deep Convolutional Neural Networks.IEEE ACCESS,8,144246-144258.
MLA	Qiu, Yuming,et al."Inferring Skin Lesion Segmentation With Fully Connected CRFs Based on Multiple Deep Convolutional Neural Networks".IEEE ACCESS 8(2020):144246-144258.