Active Learning for an Efficient Training Strategy of Computer-Aided Diagnosis Systems: Application to Diabetic Retinopathy Screening
(0) The performance of computer-aided diagnosis (CAD) systems can be highly influenced by the training strategy. CAD systems are traditionally trained using available labeled data, extracted from a specific data distribution or from public databases. Due to the wide variability of medical data, these databases might not be representative enough when the CAD system is applied to data extracted from a different clinical setting, diminishing the performance or requiring more labeled samples in order to get better data generalization. In this work, we propose the incorporation of an active learning approach in the training phase of CAD systems for reducing the number of required training samples while maximizing the system performance. The benefit of this approach has been evaluated using a specific CAD system for Diabetic Retinopathy screening. The results show that 1) using a training set obtained from a different data source results in a considerable reduction of the CAD performance; and 2) using active learning the selected training set can be reduced from 1000 to 200 samples while maintaining an area under the Receiver Operating Characteristic curve of 0.856.
C.I.Sanchez M.Niemeijer M.D.Abramoff B.van Ginneken
Department of Radiology, Radboud University Nijmegen Medical Centre,The Netherlands Image Sciences Institute, University Medical Center Utrecht, The Netherlands Department of Ophthalmology and Visual Sciences, University of Iowa, USA Department of Radiology, Radboud University Nijmegen Medical Centre, The Netherlands Image Sciences
国际会议
北京
英文
603–610
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）