  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.
