Abstract
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.
Abstract (translated)
Knee OsteoArthritis (KOA) 是一种普遍存在的骨关节炎，会导致老年人减少 mobility。然而，医生提供的诊断仍然是主观的，因为它依赖于个人经验和半定量的凯蒙德-拉森(KL)评分系统。KOA 通过使用使用深度学习技术如卷积神经网络(CNN)的计算机辅助诊断(CAD)系统而被成功诊断。在本文中，我们提出了一种新的对称神经网络，并介绍了一种用于早期检测 KOA 的新混合损失策略。模型通过将每个级别的特征提取集合中的全局平均池化层(GAP)集成起来，扩展了传统的对称神经网络。为了改善分类性能，我们提出了一种新的训练策略，将每个训练批次分为低、中和高信噪比的子集，并为每个样本分配每个新标签使用的特定混合损失函数。最后，通过将后一种损失函数与优化权重相结合，得出最终的 loss 函数。我们的测试结果表明，我们提出的这种方法显著提高了检测性能。
URL
https://arxiv.org/abs/2303.13203
