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Found 8 papers, 3 papers with code
End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers
In this study, we leveraged recent advances in Deep Learning and, using a Transformer approach, developed a unified framework for the multi-modal fusion of knee imaging data.
Unsupervised denoising for sparse multi-spectral computed tomography
However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts.}
Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning
Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials.
Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis
Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.
Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST)
Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA.
A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection
Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on society due to financial impact.
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI).
