Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability

On-field player workload exposure and knee injury risk monitoring via deep learning

In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plates and associated downstream biomechanical modeling, and many researchers have approached the problem of portability by extrapolating models built on linear statistics. An alternative approach would be to capitalize on recent advances in deep learning. In this study, using the pre-trained CaffeNet convolutional neural network (CNN) model, multivaria…

SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data

In medical image analysis, the cost of acquiring high-quality data and their annotation by experts is a barrier in many medical applications. Most of the techniques used are based on supervised learning framework and need a large amount of annotated data to achieve satisfactory performance. As an alternative, in this paper, we propose a self-supervised learning (SSL) approach to learn the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful spatial context-invariant representa…

Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR Videos

The success of deep learning based models for computer vision applications requires large scale human annotated data which are often expensive to generate. Self-supervised learning, a subset of unsupervised learning, handles this problem by learning meaningful features from unlabeled image or video data. In this paper, we propose a self-supervised learning approach to learn transferable features from MR video clips by enforcing the model to learn anatomical features. The pretext task models are designed to predict the correct ordering of the jumbled image patches that the MR video frames are d…

Knee Injury Detection using MRI with Efficiently-Layered Network (ELNet)

Magnetic Resonance Imaging (MRI) is a widely-accepted imaging technique for knee injury analysis. Its advantage of capturing knee structure in three dimensions makes it the ideal tool for radiologists to locate potential tears in the knee. In order to better confront the ever growing workload of musculoskeletal (MSK) radiologists, automated tools for patients' triage are becoming a real need, reducing delays in the reading of pathological cases. In this work, we present the Efficiently-Layered Network (ELNet), a convolutional neural network (CNN) architecture optimized for the task of initial …

MeniMV：一种多视图的半月板损伤严重程度分级基准

MeniMV: A Multi-view Benchmark for Meniscus Injury Severity Grading

MeniMV是一种多视图基准数据集，用于半月板horn损伤严重程度分级，通过提供丰富的标注数据和双视图诊断上下文，推动自动化MRI分析的发展。

基于人体关键点检测算法的前交叉韧带损伤风险评估

Assessment of Anterior Cruciate Ligament Injury Risk Based on Human Key Points Detection Algorithm

本文提出一种基于OpenPose和LESS的ACL潜在损伤风险评估算法，通过提取人体关键点特征并利用AHP加权评分模型，有效检测前交叉韧带损伤风险，为运动损伤预防提供参考。