Improving Image Classification of Knee Radiographs: An Automated Image
  Labeling Approach
Jikai Zhang Carlos Santos Christine Park Maciej Mazurowski Roy Colglazier
Large numbers of radiographic images are available in knee radiology
practices which could be used for training of deep learning models for
diagnosis of knee abnormalities. However, those images do not typically contain
readily available labels due to limitations of human annotations. The purpose
of our study was to develop an automated labeling approach that improves the
image classification model to distinguish normal knee images from those with
abnormalities or prior arthroplasty. The automated labeler was trained on a
small set of labeled data to automatically label a much larger set of unlabeled
data, further improving the image classification performance for knee
radiographic diagnosis. We developed our approach using 7,382 patients and
validated it on a separate set of 637 patients. The final image classification
model, trained using both manually labeled and pseudo-labeled data, had the
higher weighted average AUC (WAUC: 0.903) value and higher AUC-ROC values among
all classes (normal AUC-ROC: 0.894; abnormal AUC-ROC: 0.896, arthroplasty
AUC-ROC: 0.990) compared to the baseline model (WAUC=0.857; normal AUC-ROC:
0.842; abnormal AUC-ROC: 0.848, arthroplasty AUC-ROC: 0.987), trained using
only manually labeled data. DeLong tests show that the improvement is
significant on normal (p-value<0.002) and abnormal (p-value<0.001) images. Our
findings demonstrated that the proposed automated labeling approach
significantly improves the performance of image classification for radiographic
knee diagnosis, allowing for facilitating patient care and curation of large
knee datasets.