# Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks

arXiv: https://arxiv.org/abs/1708.00710 · HTML: https://mlsanity.com/paper/1708.00710/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation](https://mlsanity.com/paper/2105.12106/) (2021)
- [Co-Seg: An Image Segmentation Framework Against Label Corruption](https://mlsanity.com/paper/2102.00523/) (2021)
- [Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection](https://mlsanity.com/paper/2201.09563/) (2022)

## Abstract (arXiv)

We introduce an accurate lung segmentation model for chest radiographs based on deep convolutional neural networks. Our model is based on atrous convolutional layers to increase the field-of-view of filters efficiently. To improve segmentation performances further, we also propose a multi-stage training strategy, network-wise training, which the current stage network is fed with both input images and the outputs from pre-stage network. It is shown that this strategy has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.