20 March 2015 Reducing annotation cost and uncertainty in computer-aided diagnosis through selective iterative classification
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Abstract
Medical imaging technology has always provided radiologists with the opportunity to view and keep records of anatomy of the patient. With the development of machine learning and intelligent computing, these images can be used to create Computer-Aided Diagnosis (CAD) systems, which can assist radiologists in analyzing image data in various ways to provide better health care to patients. This paper looks at increasing accuracy and reducing cost in creating CAD systems, specifically in predicting the malignancy of lung nodules in the Lung Image Database Consortium (LIDC). Much of the cost in creating an accurate CAD system stems from the need for multiple radiologist diagnoses or annotations of each image, since there is rarely a ground truth diagnosis and even different radiologists' diagnoses of the same nodule often disagree. To resolve this issue, this paper outlines an method of selective iterative classification that predicts lung nodule malignancy by using multiple radiologist diagnoses only for cases that can benefit from them. Our method achieved 81% accuracy while costing only 46% of the method that indiscriminately used all annotations, which achieved a lower accuracy of 70%, while costing more.
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Amelia Riely, Kyle Sablan, Thomas Xiaotao, Jacob Furst, Daniela Raicu, "Reducing annotation cost and uncertainty in computer-aided diagnosis through selective iterative classification", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141K (20 March 2015); doi: 10.1117/12.2082480; https://doi.org/10.1117/12.2082480
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