论文标题
自动肺炎检测的深度学习
Deep Learning for Automatic Pneumonia Detection
论文作者
论文摘要
肺炎是幼儿死亡的主要原因，也是全球最大的死亡率之一。肺炎检测通常是通过受过良好训练的专家检查胸部X射线射线照相的。这个过程很乏味，通常导致放射科医生之间存在分歧。计算机辅助诊断系统显示了提高诊断准确性的潜力。在这项工作中，我们开发了基于单杆检测器，挤压和激发深度卷积神经网络，增强和多任务学习的肺炎区域检测的计算方法。在北美肺炎检测挑战的背景下，评估了所提出的方法，这是挑战中最好的结果之一。
Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological Society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.
