Knee osteoarthritis (OA) is a chronic disease that considerably reduces patients’ quality of life. Preventive therapies require early detection and lifetime monitoring of OA progression. In the clinical environment, the severity of OA is classified by the Kellgren and Lawrence (KL) grading system, ranging from KL-0 to KL-4. Recently, deep learning methods were applied to OA severity assessment to improve accuracy and efficiency. However, this task is still challenging due to the ambiguity between adjacent grades, especially in early-stage OA. Low confident samples, which are less representative than the typical ones, undermine the training process. Targeting the uncertainty in the OA dataset, we propose a novel learning scheme that dynamically separates the data into two sets according to their reliability. Besides, we design a hybrid loss function to help CNN learn from the two sets accordingly. With the proposed approach, we emphasize the typical samples and control the impacts of low confident cases. Experiments are conducted in a five-fold manner on five-class task and early-stage OA task. Our method achieves a mean accuracy of 70.13% on the five-class OA assessment task, which outperforms all other state-of-art methods. Despite early-stage OA detection still benefiting from the human intervention of lesion region selection, our approach achieves superior performance on the KL-0 vs. KL-2 task. Moreover, we design an experiment to validate large-scale automatic data refining during training. The result verifies the ability to characterize low confidence samples. The dataset used in this paper was obtained from the Osteoarthritis Initiative.

中文翻译：

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从高度可信的样本中学习以进行自动膝骨关节炎严重程度评估：来自骨关节炎倡议的数据


膝骨关节炎（OA）是一种慢性疾病，会大大降低患者的生活质量。预防性治疗需要早期发现 OA 进展并进行终生监测。在临床环境中，OA的严重程度按照Kellgren and Lawrence (KL)分级系统进行分类，范围从KL-0到KL-4。最近，深度学习方法被应用于OA严重程度评估，以提高准确性和效率。然而，由于相邻等级之间的模糊性，这项任务仍然具有挑战性，特别是在早期 OA 中。低置信样本比典型样本的代表性较差，会破坏训练过程。针对 OA 数据集中的不确定性，我们提出了一种新颖的学习方案，根据数据的可靠性将数据动态分为两组。此外，我们设计了一个混合损失函数来帮助 CNN 从这两个集合中进行相应的学习。通过所提出的方法，我们强调典型样本并控制低置信度案例的影响。在五类任务和早期OA任务上分五次进行实验。我们的方法在五级 OA 评估任务上的平均准确率达到 70.13%，优于所有其他最先进的方法。尽管早期 OA 检测仍然受益于病变区域选择的人为干预，但我们的方法在 KL-0 与 KL-2 任务上取得了优异的性能。此外，我们设计了一个实验来验证训练期间的大规模自动数据精炼。结果验证了表征低置信度样本的能力。本文使用的数据集来自骨关节炎倡议。