Deep active learning for classifying cancer pathology reports.
Kevin De AngeliShang GaoMohammed AlawadHong-Jun YoonNoah SchaefferkoetterXiao-Cheng WuEric B DurbinJennifer DohertyAntoinette StroupLinda CoyleLynne PenberthyGeorgia TourassiPublished in: BMC bioinformatics (2021)
Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.