Abstract
Computer-Aided Diagnosis systems have been used as second readers in the medical imaging diagnostic process. In this study, we aim to identify cases that are hard to diagnose and lead to interpretation variability among medical experts. We propose a combination of image features and advanced machine learning classifiers to predict the degree of malignancy and determine the level of diagnostic difficulty by looking where these classifiers collectively fail. Using the NIH/NCI Lung Image Database Consortium (LIDC) dataset and four ensemble learning algorithms (bagging, random forest, AdaBoost, and a heterogeneous ensemble with decision trees, support vector machines, and k-nearest neighbors), our results show that we can not only detect difficult cases, but we are also able to identify what imaging characteristics or features make these cases hard to diagnostically interpret.
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Yu, Y., Wang, Y., Furst, J., Raicu, D. (2019). Identifying Diagnostically Complex Cases Through Ensemble Learning. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_27
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DOI: https://doi.org/10.1007/978-3-030-27272-2_27
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