Robust active learning for the diagnosis of parasites
作者：
Highlights：
• A robust active learning method, called RDS, based on a priori data organization.
• RDS properly balances sample diversity and uncertainty for useful sample selection.
• It provides high classification accuracy for the automated diagnosis of parasites.
• Comparisons with different clustering, classification and other literature methods.
• RDS was evaluated by an experienced expert in parasitology using a realistic scenario.
摘要
Highlights•A robust active learning method, called RDS, based on a priori data organization.•RDS properly balances sample diversity and uncertainty for useful sample selection.•It provides high classification accuracy for the automated diagnosis of parasites.•Comparisons with different clustering, classification and other literature methods.•RDS was evaluated by an experienced expert in parasitology using a realistic scenario.
论文关键词：Active learning,Pattern recognition,Automated diagnosis of intestinal parasites,Microscopy image analysis,Optimum-path forest classifiers
论文评审过程：Received 9 April 2014, Revised 9 March 2015, Accepted 18 May 2015, Available online 28 May 2015, Version of Record 16 July 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.05.020
