论文标题
使用MURA数据集检测X射线骨异常
X-Ray bone abnormalities detection using MURA dataset
论文作者
论文摘要
我们介绍了2017年斯坦福大学（Stanford University）的MURA数据集中训练的深网。我们的系统能够检测到X光片上的骨异常并可视化此类区域。 We found that our solution has the accuracy comparable to the best results that have been achieved by other development teams that used MURA dataset, in particular the overall Kappa score that was achieved by our team is about 0.942 on the wrist, 0.862 on the hand and o.735 on the shoulder (compared to the best available results to this moment on the official web-site 0.931, 0.851 and 0.729 accordingly).但是，尽管取得了良好的效果，但仍有很多方向可以增强所提出的技术。我们认为，进一步开发的计算机辅助系统（CAD）的X光片具有很大的潜力，这将有助于实践专家诊断骨骼骨折以及骨肿瘤学病例的速度更快且精度更高。
We introduce the deep network trained on the MURA dataset from the Stanford University released in 2017. Our system is able to detect bone abnormalities on the radiographs and visualise such zones. We found that our solution has the accuracy comparable to the best results that have been achieved by other development teams that used MURA dataset, in particular the overall Kappa score that was achieved by our team is about 0.942 on the wrist, 0.862 on the hand and o.735 on the shoulder (compared to the best available results to this moment on the official web-site 0.931, 0.851 and 0.729 accordingly). However, despite the good results there are a lot of directions for the future enhancement of the proposed technology. We see a big potential in the further development computer aided systems (CAD) for the radiographs as the one that will help practical specialists diagnose bone fractures as well as bone oncology cases faster and with the higher accuracy.
