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Found 3 papers, 1 papers with code
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
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.
Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance
We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score.
Malignancy Prediction and Lesion Identification from Clinical Dermatological Images
We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture.
