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首页> 外文会议>International Conference on Transdisciplinary AI >Object Detection and Segmentation in Chest X-rays for Tuberculosis Screening
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Object Detection and Segmentation in Chest X-rays for Tuberculosis Screening

机译：胸部X光检查中的目标检测和分割以筛查肺结核

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摘要

Tuberculosis (TB) is a contagious disease leading to the deaths of approximately 2 million people annually. Providing healthcare professionals with better information, at a faster pace, is essential for combating this disease, especially in Low and Middle Income Countries with resource-constrained health systems. In this paper we describe how using convolution neural networks (CNNs) with an object level annotated dataset of chest X-rays (CXRs) allows us to identify the location of pulmonary issues indicative of TB. We compare the performance of Faster R-CNN, Mask R-CNN, and Cascade versions of each, demonstrating reasonable results with a small dataset. We present a method to reduce the false positive rate by comparing the location of a detected object with the known location of areas where the detected class is likely to occur in the lung. Our results show that with a dataset of high-quality, object level annotations, object detection and segmentation of CXRs is possible and could be used as part of an automated TB screening process. This work has the potential to improve the speed of TB diagnosis, if implemented within the corresponding health care system and adapted to existing clinical worktlows.
机译：结核病（TB）是一种传染性疾病，每年导致约200万人死亡。为医护人员提供更快的更好的信息对于抗击这种疾病至关重要，特别是在资源受限的医疗系统的中低收入国家。在本文中，我们描述了如何将卷积神经网络（CNN）与对象水平注释的胸部X射线（CXRs）数据集结合使用，从而使我们能够识别出指示结核病的肺部疾病的位置。我们比较了Faster R-CNN，Mask R-CNN和各个Cascade版本的性能，并通过较小的数据集展示了合理的结果。我们提出了一种通过将检测到的物体的位置与肺中可能发生检测到的类别的区域的已知位置进行比较来降低误报率的方法。我们的结果表明，使用高质量的对象级注释数据集，可以对CXR进行对象检测和分割，并且可以将其用作自动化TB筛选过程的一部分。如果在相应的卫生保健系统中实施并适应于现有的临床工作，则这项工作有可能提高结核病诊断的速度。

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