# Pneumonia Detection in Chest Radiographs

arXiv: https://arxiv.org/abs/1811.08939 · HTML: https://mlsanity.com/paper/1811.08939/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

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

- [Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs](https://mlsanity.com/paper/2205.04282/) (2022)
- [MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis](https://mlsanity.com/paper/2110.06063/) (2021)
- [Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis](https://mlsanity.com/paper/2111.08606/) (2021)

## Abstract (arXiv)

In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only open-source deep learning object detection and is based on CoupleNet, a fully convolutional network which incorporates global and local features for object detection. Our approach achieves robustness through critical modifications of the training process and a novel ensembling algorithm which merges bounding boxes from several models. We tested our detection algorithm tested on a dataset of 3000 chest radiographs as part of the 2018 RSNA Pneumonia Challenge; our solution was recognized as a winning entry in a contest which attracted more than 1400 participants worldwide.