Title: Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach Show Chinese title

Title: 改善膝关节X光图像分类：一种自动图像标注方法

Abstract: 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. Show Chinese abstract

Abstract: 膝关节放射学实践中存在大量可用于训练深度学习模型以诊断膝关节异常的影像资料。然而，由于人工标注的限制，这些图像通常没有现成的标签。本研究的目的在于开发一种自动标记方法，改进图像分类模型以区分正常的膝关节图像与异常或之前进行过关节置换的图像。自动标记器在少量标记数据上进行训练，以自动标记更大规模的未标记数据，进一步提升膝关节放射学诊断的图像分类性能。我们使用了7,382名患者的数据开发此方法，并在另一组637名患者的数据上进行了验证。最终的图像分类模型使用手动标记和伪标记的数据进行训练，在所有类别（正常AUC-ROC：0.894；异常AUC-ROC：0.896；关节置换AUC-ROC：0.990）的加权平均AUC（WAUC：0.903）值高于基线模型（WAUC=0.857；正常AUC-ROC：0.842；异常AUC-ROC：0.848；关节置换AUC-ROC：0.987），后者仅使用手动标记的数据进行训练。Delong检验显示，这种改进在正常（p值<0.002）和异常（p值<0.001）图像上具有显著性。我们的研究结果表明，所提出的自动标记方法显著提升了放射学膝关节诊断的图像分类性能，有助于改善患者护理并整理大规模膝关节数据集。