Knee osteoarthritis (KOA) is a widespread condition that can cause chronic
pain and stiffness in the knee joint. Early detection and diagnosis are crucial
for successful clinical intervention and management to prevent severe
complications, such as loss of mobility. In this paper, we propose an automated
approach that employs the Swin Transformer to predict the severity of KOA. Our
model uses publicly available radiographic datasets with Kellgren and Lawrence
scores to enable early detection and severity assessment. To improve the
accuracy of our model, we employ a multi-prediction head architecture that
utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel
training approach that reduces the data drift between multiple datasets to
ensure the generalization ability of the model. The results of our experiments
demonstrate the effectiveness and feasibility of our approach in predicting KOA
severity accurately.

提出一种使用 Swin Transformer 对膝关节骨关节炎 (KOA) 进行严重程度预测的自动化方法，使用公开的放射性数据集和 Kellgren 以及 Lawrence 分级来提前检测和评估疾病的严重程度，并通过多预测头架构的多层感知机分类器、降低数据漂移的训练方法，提高模型的准确性和泛化能力。实验证明该方法能够准确预测 KOA 的严重程度。

利用 Swin 转换器自动诊断膝骨关节炎严重程度

Automatic diagnosis of knee osteoarthritis severity using Swin  transformer

The goal of video motion magnification techniques is to magnify small motions
in a video to reveal previously invisible or unseen movement. Its uses extend
from bio-medical applications and deep fake detection to structural modal
analysis and predictive maintenance. However, discerning small motion from
noise is a complex task, especially when attempting to magnify very subtle
often sub-pixel movement. As a result, motion magnification techniques
generally suffer from noisy and blurry outputs. This work presents a new
state-of-the-art model based on the Swin Transformer, which offers better
tolerance to noisy inputs as well as higher-quality outputs that exhibit less
noise, blurriness and artifacts than prior-art. Improvements in output image
quality will enable more precise measurements for any application reliant on
magnified video sequences, and may enable further development of video motion
magnification techniques in new technical fields.

本研究提出了一种基于 Swine Transformer 的视频运动放大技术的新模型，具有更好的对噪声输入的容忍性和输出高品质荧光的能力，可用于在任何依赖放大视频序列的应用程序中实现更精确的测量，为视频运动放大技术在新技术领域的进一步发展打下基础。