Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
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Developing machine learning models for radiology requires large-scale imaging data sets with labels for abnormalities, but the process is challenging due to the size and complexity of the data as well as the cost of labeling. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 20,201 unique patients. This is the largest multiply-annotated chest CT data set reported. To annotate this data set, we developed a rule-based method for automatically extracting abnormality labels from radiologist free-text reports with an average F-score of 0.976 (min 0.941, max 1.0). We also developed a model for multilabel abnormality classification of chest CT volumes that uses a deep convolutional neural network (CNN). This model reached a classification performance of AUROC greater than 0.90 for 18 abnormalities, with an average AUROC of 0.773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data. We show that training on more labels improves performance significantly: for a subset of 9 labels - nodule, opacity, atelectasis, pleural effusion, consolidation, mass, pericardial effusion, cardiomegaly, and pneumothorax - the model's average AUROC increased by 10 percent when the number of training labels was increased from 9 to all 83. All code for volume preprocessing, automated label extraction, and the volume abnormality prediction model will be made publicly available. The 36,316 CT volumes and labels will also be made publicly available pending institutional approval.
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