Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis
作者：
Highlights：
• An unsupervised method with crowdsourced data to detect forms in images is proposed.
• The procedure consists of a clustering and a detection stage based on the EM algorithm.
• The method accounts for outliers and is robust to unreliable annotators.
• An online implementation of the method suited for streaming data is presented.
• Experimental results with real data for Malaria diagnose support the approach.
摘要
•An unsupervised method with crowdsourced data to detect forms in images is proposed.•The procedure consists of a clustering and a detection stage based on the EM algorithm.•The method accounts for outliers and is robust to unreliable annotators.•An online implementation of the method suited for streaming data is presented.•Experimental results with real data for Malaria diagnose support the approach.
论文关键词：Crowdsourcing,Unreliable annotators,Unsupervised method,Online EM algorithm,MalariaSpot
论文评审过程：Received 3 January 2018, Revised 6 July 2018, Accepted 5 September 2018, Available online 13 September 2018, Version of Record 23 September 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.09.001
