Mansoor Awais, Perez Geovanny, Nino Gustavo, Linguraru Marius George
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:97-100. doi: 10.1109/EMBC.2016.7590649.
Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT. However, the subtlety of textures in CXR makes them hard to discern even by trained eye. We explore the performance of deep learning abnormal tissue characterization from CXR. Prior studies have used CT imaging to characterize air trapping in subjects with pulmonary disease; however, the use of CT in children is not recommended mainly due to concerns pertaining to radiation dosage. In this work, we present a stacked autoencoder (SAE) deep learning architecture for automated tissue characterization of air-trapping from CXR. To our best knowledge this is the first study applying deep learning framework for the specific problem on 51 CXRs, an F-score of ≈ 76.5% and a strong correlation with the expert visual scoring (R=0.93, p =<; 0.01) demonstrate the potential of the proposed method to characterization of air trapping.
近年来，计算机辅助诊断计算机断层扫描（CT）图像中的肺部纹理异常取得了重大进展。而在胸部X光片（CXR）这一肺部诊断常用模式方面的类似举措则发展得少得多。胸部X光片对于诊断而言是快速、经济且低辐射的解决方案，优于CT。然而，胸部X光片中纹理的细微之处使得即使是训练有素的人眼也难以辨别。我们探索了深度学习从胸部X光片中进行异常组织特征识别的性能。先前的研究使用CT成像来表征患有肺部疾病的受试者的气体潴留情况；然而，由于对辐射剂量的担忧，不建议在儿童中使用CT。在这项工作中，我们提出了一种堆叠自编码器（SAE）深度学习架构，用于从胸部X光片中自动进行气体潴留的组织特征识别。据我们所知，这是第一项针对51张胸部X光片上的特定问题应用深度学习框架的研究，约76.5%的F分数以及与专家视觉评分的强相关性（R = 0.93，p <= 0.01）证明了所提出方法在表征气体潴留方面的潜力。
