论文标题
使用resnet34作为U-NET的骨架，用于胸部X射线的第2套胸部X射线分割
The 2ST-UNet for Pneumothorax Segmentation in Chest X-Rays using ResNet34 as a Backbone for U-Net
论文作者
论文摘要
气胸，也称为塌陷的肺部，是指肺和胸壁之间胸膜空间中空气的存在。它可能很小（不需要治疗），也可能很大，如果未及时识别和治疗，会导致死亡。专家使用胸部X射线很容易看到和识别它。尽管此方法主要是无错误的，但它是耗时的，需要专家放射科医生。最近，计算机视觉在检测和细分气胸方面提供了大力帮助。在本文中，我们提出了一个2阶段的训练系统（第2-UNET），以分割带有气胸的图像。该系统是基于U-NET构建的，其剩余网络（RESNET-34）主链已在Imagenet数据集上进行了预训练。在加载训练有素的模型权重以更高的分辨率重新训练网络之前，我们从较低的分辨率训练网络开始。此外，我们利用不同的技术，包括随机重量平均（SWA），数据增强和测试时间增强（TTA）。我们使用2019年SIIM-ACR PNEUMOTHORAX分割挑战提供的胸部X射线数据集，该数据集包含12,047张培训图像和3,205个测试图像。我们的实验表明，2阶段训练可以提高网络收敛的更好，更快。我们的方法达到了0.8356平均骰子相似性系数（DSC），将其置于1,475分中124个模型的前9％。
Pneumothorax, also called a collapsed lung, refers to the presence of the air in the pleural space between the lung and chest wall. It can be small (no need for treatment), or large and causes death if it is not identified and treated on time. It is easily seen and identified by experts using a chest X-ray. Although this method is mostly error-free, it is time-consuming and needs expert radiologists. Recently, Computer Vision has been providing great assistance in detecting and segmenting pneumothorax. In this paper, we propose a 2-Stage Training system (2ST-UNet) to segment images with pneumothorax. This system is built based on U-Net with Residual Networks (ResNet-34) backbone that is pre-trained on the ImageNet dataset. We start with training the network at a lower resolution before we load the trained model weights to retrain the network with a higher resolution. Moreover, we utilize different techniques including Stochastic Weight Averaging (SWA), data augmentation, and Test-Time Augmentation (TTA). We use the chest X-ray dataset that is provided by the 2019 SIIM-ACR Pneumothorax Segmentation Challenge, which contains 12,047 training images and 3,205 testing images. Our experiments show that 2-Stage Training leads to better and faster network convergence. Our method achieves 0.8356 mean Dice Similarity Coefficient (DSC) placing it among the top 9% of models with a rank of 124 out of 1,475.
