20 March 2015 An outlier filtering approach for machine sourced weak segmentations
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Abstract
Analysis of medical images by radiologists is often a time consuming and costly process. Computer Aided diagnosis (CAD) systems can be used to provide second opinion and assist radiologists in diagnosis. Crowdsourcing has been a tool used by many domains in order to generate fast and cheap labels. However, exposing medical images to a large crowd is problematic. Our system takes a machine sourcing approach to CADs – that is, the idea that multiple weak segmentations can be used to create predictions with the same quality as an expert. We propose that the use of a large amount of weak segmentations can simulate crowdsourcing to create accurate predictions of semantic characteristics. In addition, we investigated the idea that outliers filtering will better performance of this CAD. Three segmentation algorithms were employed to create 20 weak segmentations. Low quality segmentations were then removed using outlier filtering method. Segmentations were then classified using an ensemble classifier to create final predictions. It was found that this CAD preformed just as well or better than a radiologist. In contrast, the removal of outliers from the set of segmentations does not improve the result further.
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Elza Margolin, Jacob Furst, Daniela Raicu, "An outlier filtering approach for machine sourced weak segmentations", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142Q (20 March 2015); doi: 10.1117/12.2082464; https://doi.org/10.1117/12.2082464
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