Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological Society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.
@article{arxiv.2005.13899,
title = {Deep Learning for Automatic Pneumonia Detection},
author = {Tatiana Gabruseva and Dmytro Poplavskiy and Alexandr A. Kalinin},
journal= {arXiv preprint arXiv:2005.13899},
year = {2020}
}