论文标题
移动的Windows变形金刚用于医疗图像质量评估
Shifted Windows Transformers for Medical Image Quality Assessment
论文作者
论文摘要
为了在医学成像研究中保持标准，图像应具有必要的图像质量，以进行潜在的诊断使用。尽管基于CNN的方法用于评估图像质量，但其性能仍然可以从准确性方面提高。在这项工作中，我们通过使用SWIN Transformer解决了此问题，这改善了导致医疗图像质量下降的质量质量不佳的分类性能。我们在胸部X射线（Object-CXR）上测试了对异物分类问题的方法，并在心脏MRI上使用四腔视图（LVOT）测试了心脏MRI上的左心室流出区分类问题。虽然我们在对象CXR和LVOT数据集上获得了87.1％和95.48％的分类精度，但我们的实验结果表明，使用SWIN Transformer的使用可以提高对象CXR分类性能，同时获得LVOT数据集的可比性能。据我们所知，我们的研究是医学图像质量评估的第一个Vision Transformer应用程序。
To maintain a standard in a medical imaging study, images should have necessary image quality for potential diagnostic use. Although CNN-based approaches are used to assess the image quality, their performance can still be improved in terms of accuracy. In this work, we approach this problem by using Swin Transformer, which improves the poor-quality image classification performance that causes the degradation in medical image quality. We test our approach on Foreign Object Classification problem on Chest X-Rays (Object-CXR) and Left Ventricular Outflow Tract Classification problem on Cardiac MRI with a four-chamber view (LVOT). While we obtain a classification accuracy of 87.1% and 95.48% on the Object-CXR and LVOT datasets, our experimental results suggest that the use of Swin Transformer improves the Object-CXR classification performance while obtaining a comparable performance for the LVOT dataset. To the best of our knowledge, our study is the first vision transformer application for medical image quality assessment.
