论文标题
对现有和新的深度学习方法的比较研究，用于使用mRNET数据集检测膝关节受伤
A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset
论文作者
论文摘要
这项工作通过利用斯坦福大学的MRNET数据集进行了对现有技术和新技术的比较研究，以检测膝关节受伤。所有方法均基于深度学习，我们探讨了转移学习的比较性能以及从头开始训练的深度残留网络。我们还通过例如使用固定数量的切片或2D图像来利用磁共振成像（MRI）数据的某些特征，从轴向，冠状和矢状平面以及将三个平面组合到一个多平面网络中。总体而言，我们通过使用最新的深度学习体系结构和数据增强策略在验证数据上实现了93.4％的AUC。还提出了更灵活的体系结构，可能有助于对处理MRI的模型的开发和培训。我们发现，转移学习和精心调整的数据增强策略是确定最佳性能的关键因素。
This work presents a comparative study of existing and new techniques to detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are based on deep learning and we explore the comparative performances of transfer learning and a deep residual network trained from scratch. We also exploit some characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using a fixed number of slices or 2D images from each of the axial, coronal and sagittal planes as well as combining the three planes into one multi-plane network. Overall we achieved a performance of 93.4% AUC on the validation data by using the more recent deep learning architectures and data augmentation strategies. More flexible architectures are also proposed that might help with the development and training of models that process MRIs. We found that transfer learning and a carefully tuned data augmentation strategy were the crucial factors in determining best performance.
