As an analytic tool in medicine, particularly in radiology, deep learning is gaining much attention and opening a new way for disease diagnosis. Nonetheless, it is rather challenging to acquire large-scale detailed labelled datasets in the field of medical imaging. In fact, transfer learning provides a possible way to resolve this issue to a certain extent such that the parameter learning of a neural network starts with its pre-trained weights learned from a large-scale dataset of certain similar task, and fine-tunes on a small comprehensively annotated dataset for the particular target task. The main aim of this study is to apply the deep learning model to detect the synovial fluid of human knee joint from magnetic resonance images. A specialized convolutional neural network architecture is proposed for automated detection of human knee joint's synovial fluid. Two independent datasets are used in the training, development, and evaluation of the proposed model. It is demonstrated by the experimental results that the proposed model obtains high sensitivity, specificity, precision, and accuracy to the detection of human knee joint's synovial fluid. As a result, this proposed approach provides a novel and feasible way for automating and expediting the synovial fluid analysis.

中文翻译：

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基于深度学习的磁共振图像通过转移学习自动检测人膝关节滑液

作为医学（尤其是放射学）的分析工具，深度学习正受到广泛关注，并为疾病诊断开辟了新途径。然而，在医学成像领域中获取大规模的详细标记数据集是相当具有挑战性的。实际上，转移学习提供了在某种程度上解决该问题的可能方法，从而使神经网络的参数学习从从某些类似任务的大规模数据集中学习到的预训练权重开始，然后进行微调。一个针对特定目标任务的全面注释的小型数据集。这项研究的主要目的是应用深度学习模型从磁共振图像中检测人膝关节的滑液。提出了一种专门的卷积神经网络架构，用于自动检测人的膝关节。滑液。在模型的训练，开发和评估中使用了两个独立的数据集。实验结果表明，该模型对人膝关节滑液的检测具有较高的灵敏度，特异性，精密度和准确性。结果，该提出的方法为自动和加速滑液分析提供了一种新颖可行的方法。