基于自监督特征提取的骨骼X线影像异常检测方法

Anomaly detection method for skeletal X-ray images based on self-supervised feature extraction

为探索自监督特征提取方法在骨骼X线影像异常检测方面的可行性,提出了基于自监督特征提取的骨骼X线影像异常检测方法。将自监督学习框架与ViT(Vision Transformer)模型结合用于骨骼异常检测的特征提取,并通过线性分类器进行异常检测分类,在特征提取阶段可有效避免有监督模型对大规模有标注数据的依赖性。在公开的骨骼X线影像数据集上进行实验,采用准确率分别评估预训练的卷积神经网络(CNN)和自监督特征提取的骨骼异常检测模型。实验结果表明,自监督特征提取模型相较于一般的CNN模型效果更优,在7个部位分类结果与有监督的CNN模型ResNet50相差无几,但在肘部、手指、肱骨的异常检测中准确率均取得了最优值,平均准确率提升了5.37个百分点。所提方法易于实现,可以作为放射科医生初步诊断的可视化辅助工具。

In order to explore the feasibility of a self-supervised feature extraction method in skeletal X-ray image anomaly detection,an anomaly detection method for skeletal X-ray images based on self-supervised feature extraction was proposed.The self-supervised learning framework and Vision Transformer(ViT)model were combined for feature extraction in skeletal anomaly detection,and anomaly detection classification was carried out by linear classifiers,which can effectively avoid the dependence of supervised models on large-scale labeled data in feature extraction stage.Experiments were performed on publicly available skeletal X-ray image datasets,the skeletal anomaly detection models based on pre-trained Convolutional Neural Network(CNN)and self-supervised feature extraction were evaluated with accuracy.Experimental results show that self-supervised feature extraction model has better effect than the general CNN models,its classification results in seven parts are similar to those of supervised CNN models,but the abnormal detection accuracy for elbow,finger and humerus achieved optimal values,and the average accuracies increases by 5.37 percentage points compared to ResNet50.The proposed method is easy to implement and can be used as a visual assistant tool for radiologist initial diagnosis.