Method and apparatus for selecting radiology reports for image labeling by modality and anatomical region of interest

A system for developing a classification model, the system comprising:

• one or more electronic processors configured to:
• select, from a corpus of reports, a subset of the reports from which to form a training set and a testing set,
• assign labels of a modality and an anatomical focus to the reports in both the training set and the testing set, wherein assigning labels of the modality and the anatomical focus to the reports including assigning a binary classification distinguishing chest x-ray reports from non-chest x-ray reports and, for each report labeled as a non-chest x-ray report, assigning a multiclass classification defining a modality associated with the report,
• extract a sparse representation matrix for each of the training set and the testing set based on features in the training set,
• learn a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model,
• test the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set, and
• predict, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the subset.

A non-transitory, computer-readable medium storing instructions that, when executed by one or more electronic processors, perform a set of function, the set of functions comprising:

• selecting, from a corpus of reports, a subset of the reports from which to form a training set and a testing set;
• assigning labels of a modality and an anatomical focus to the reports in both the training set and the testing set, wherein assigning labels of the modality and the anatomical focus to the reports including assigning a binary classification distinguishing chest x-ray reports from non-chest x-ray reports and, for each report labeled as a non-chest x-ray report, assigning a multiclass classification defining a modality associated with the report;
• extracting a sparse representation matrix for each of the training set and the testing set based on features in the training set;
• learning a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model;
• testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set;
• and
• predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the subset.