Pneumonia Detection in Chest Radiographs
This work addresses pneumonia diagnosis in medical imaging, which is an incremental improvement in a domain-specific application.
The authors tackled pneumonia detection and localization in chest radiographs using a deep learning object detection method based on CoupleNet, achieving a winning entry in the 2018 RSNA Pneumonia Challenge with over 1400 participants.
In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only \emph{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.