Knee OsteoArthritis (KOA) is a prevalent musculoskeletal disorder that causes decreased mobility in seniors. The diagnosis provided by physicians is subjective, however, as it relies on personal experience and the semi-quantitative Kellgren-Lawrence (KL) scoring system. KOA has been successfully diagnosed by Computer-Aided Diagnostic (CAD) systems that use deep learning techniques like Convolutional Neural Networks (CNN). In this paper, we propose a novel Siamese-based network, and we introduce a new hybrid loss strategy for the early detection of KOA. The model extends the classical Siamese network by integrating a collection of Global Average Pooling (GAP) layers for feature extraction at each level. Then, to improve the classification performance, a novel training strategy that partitions each training batch into low-, medium- and high-confidence subsets, and a specific hybrid loss function are used for each new label attributed to each sample. The final loss function is then derived by combining the latter loss functions with optimized weights. Our test results demonstrate that our proposed approach significantly improves the detection performance.

翻译：膝骨关节炎（KOA）是一种常见的肌肉骨骼疾病，会导致老年人活动能力下降。然而，医师的诊断具有主观性，因为它依赖于个人经验和半定量的Kellgren-Lawrence（KL）分级系统。使用卷积神经网络（CNN）等深度学习技术的计算机辅助诊断（CAD）系统已成功实现了KOA的诊断。本文提出了一种新颖的基于孪生网络的架构，并引入了一种新的混合损失策略用于KOA的早期检测。该模型通过在每个层级集成全局平均池化（GAP）层进行特征提取，扩展了经典孪生网络。为了提升分类性能，我们提出了一种新的训练策略，将每个训练批次划分为低置信度、中置信度和高置信度子集，并对每个样本赋予的新标签使用特定的混合损失函数。最终损失函数通过将上述损失函数与优化权重相结合得到。测试结果表明，我们提出的方法显著提升了检测性能。