School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
BMC Med Imaging. 2021 Jun 10;21(1):99. doi: 10.1186/s12880-021-00627-y.
Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological information, which is vital for thoracic disease classification. Inspired by this, we propose a novel deep learning framework to explore discriminative information from lung and heart regions.
We design a feature extractor equipped with a multi-scale attention module to learn global attention maps from global images. To exploit disease-specific cues effectively, we locate lung and heart regions containing pathological information by a well-trained pixel-wise segmentation model to generate binarization masks. By introducing element-wise logical AND operator on the learned global attention maps and the binarization masks, we obtain local attention maps in which pixels are are 1 for lung and heart region and 0 for other regions. By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions. Compared to existing methods fusing global and local features, we adopt feature weighting to avoid weakening visual cues unique to lung and heart regions. Our method with pixel-wise segmentation can help overcome the deviation of locating local regions. Evaluated by the benchmark split on the publicly available chest X-ray14 dataset, the comprehensive experiments show that our method achieves superior performance compared to the state-of-the-art methods.
We propose a novel deep framework for the multi-label classification of thoracic diseases in chest X-ray images. The proposed network aims to effectively exploit pathological regions containing the main cues for chest X-ray screening. Our proposed network has been used in clinic screening to assist the radiologists. Chest X-ray accounts for a significant proportion of radiological examinations. It is valuable to explore more methods for improving performance.
胸部 X 光检查是最常用和最经济的胸部疾病筛查放射学检查。根据筛查胸部 X 光的领域知识，病理信息通常位于肺部和心脏区域。然而，在实践中获取区域级别的注释是昂贵的，并且模型训练主要依赖于图像级别的弱监督类标签，这对计算机辅助胸部 X 光筛查具有很大的挑战性。为了解决这个问题，最近提出了一些方法来识别包含病理信息的局部区域，这对胸部疾病分类至关重要。受此启发，我们提出了一种新的深度学习框架，从肺部和心脏区域中探索有区别的信息。
我们设计了一个特征提取器，配备了一个多尺度注意力模块，从全局图像中学习全局注意力图。为了有效地利用疾病特异性线索，我们通过训练有素的像素级分割模型定位包含病理信息的肺部和心脏区域，生成二值化掩模。通过在学习到的全局注意力图和二值化掩模上引入元素级逻辑与操作，我们得到了局部注意力图，其中肺部和心脏区域的像素值为 1，其他区域的像素值为 0。通过在注意力图中对非肺部和心脏区域的特征进行置零，可以有效地利用它们在肺部和心脏区域中的疾病特异性线索。与融合全局和局部特征的现有方法相比，我们采用特征加权来避免削弱肺部和心脏区域特有的视觉线索。我们的方法使用像素级分割，可以帮助克服定位局部区域的偏差。在公开的胸部 X 射线 14 数据集的基准分割上进行评估，综合实验表明，与最先进的方法相比，我们的方法表现出优越的性能。
我们提出了一种新的深度学习框架，用于胸部 X 射线图像中的多标签胸部疾病分类。所提出的网络旨在有效地利用包含胸部 X 光筛查主要线索的病理区域。我们提出的网络已用于临床筛查，以协助放射科医生。胸部 X 光检查在放射学检查中占有很大比例。探索更多提高性能的方法是有价值的。
