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Automatic annotation of radiological observations in liver CT images
2012
AMIA Annual Symposium Proceedings
We aim to predict radiological observations using computationally-derived imaging features extracted from computed tomography (CT) images. We created a dataset of 79 CT images containing liver lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Traditional logistic regression was compared to L(1)-regularized logistic regression (LASSO) in
pmid:23304295
pmcid:PMC3540508
fatcat:d47tnclsgjdxpbzogqfltse6ie