English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Pneumothorax, also called a collapsed lung, refers to the presence of the airin the pleural space between the lung and chest wall. It can be small (no needfor treatment), or large and causes death if it is not identified and treatedon 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 needsexpert radiologists. Recently, Computer Vision has been providing greatassistance in detecting and segmenting pneumothorax. In this paper, we proposea 2-Stage Training system (2ST-UNet) to segment images with pneumothorax. Thissystem is built based on U-Net with Residual Networks (ResNet-34) backbone thatis pre-trained on the ImageNet dataset. We start with training the network at alower resolution before we load the trained model weights to retrain thenetwork with a higher resolution. Moreover, we utilize different techniquesincluding Stochastic Weight Averaging (SWA), data augmentation, and Test-TimeAugmentation (TTA). We use the chest X-ray dataset that is provided by the 2019SIIM-ACR Pneumothorax Segmentation Challenge, which contains 12,047 trainingimages and 3,205 testing images. Our experiments show that 2-Stage Trainingleads to better and faster network convergence. Our method achieves 0.8356 meanDice Similarity Coefficient (DSC) placing it among the top 9% of models with arank of 124 out of 1,475.

The 2ST-UNet for Pneumothorax Segmentation in Chest X-Rays using ResNet34 as a Backbone for U-Net