  Machine learning models for radiology benefit from large-scale data sets with
high quality labels for abnormalities. We curated and analyzed a chest computed
tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is
the largest multiply-annotated volumetric medical imaging data set reported. To
annotate this data set, we developed a rule-based method for automatically
extracting abnormality labels from free-text radiology reports with an average
F-score of 0.976 (min 0.941, max 1.0). We also developed a model for
multi-organ, multi-disease 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% 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.
