论文标题
使用三脚架网络的颈椎成熟（CVM）阶段的分类
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network
论文作者
论文摘要
我们提出了一种新颖的深度学习方法，用于颈椎成熟（CVM）阶段的全自动检测和分类。深卷积神经网络由三个平行网络（Tripodnet）组成，该网络（三脚架）独立训练了具有不同初始化参数的训练。他们还具有内置的一组新型定向过滤器，可突出X射线图像中的宫颈垂直边缘。三个平行网络的输出使用完全连接的层组合。 1018头射线照相仪被标记，按性别划分，并根据CVM阶段进行分类。最终使用不同的训练技术和补丁的图像用于训练三脚架以及一组可调的方向边缘增强器。实施数据增强以避免过度拟合。 Tripodnet在女性患者中达到了81.18％的最新精度，男性患者的最新准确性为75.32 \％。所提出的Tripodnet在数据集中的精度比SWIN变形金刚和我们研究的CVM阶段估计的先前网络模型更高。
We present a novel deep learning method for fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. The deep convolutional neural network consists of three parallel networks (TriPodNet) independently trained with different initialization parameters. They also have a built-in set of novel directional filters that highlight the Cervical Verte edges in X-ray images. Outputs of the three parallel networks are combined using a fully connected layer. 1018 cephalometric radiographs were labeled, divided by gender, and classified according to the CVM stages. Resulting images, using different training techniques and patches, were used to train TripodNet together with a set of tunable directional edge enhancers. Data augmentation is implemented to avoid overfitting. TripodNet achieves the state-of-the-art accuracy of 81.18\% in female patients and 75.32\% in male patients. The proposed TripodNet achieves a higher accuracy in our dataset than the Swin Transformers and the previous network models that we investigated for CVM stage estimation.
