Deep Learning for Automatic Pneumonia Detection

Tatiana Gabruseva, Dmytro Poplavskiy, Alexandr A. Kalinin

CVPRW 2020 pp. 1436-1443

doi:10.1109/CVPRW50498.2020.00183 /cvprw/2020/gabruseva2020cvprw-deep/

Abstract

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-extinction 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.

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Cite

Text

Gabruseva et al. "Deep Learning for Automatic Pneumonia Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00183

Markdown

[Gabruseva et al. "Deep Learning for Automatic Pneumonia Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/gabruseva2020cvprw-deep/) doi:10.1109/CVPRW50498.2020.00183

BibTeX

@inproceedings{gabruseva2020cvprw-deep,
  title     = {{Deep Learning for Automatic Pneumonia Detection}},
  author    = {Gabruseva, Tatiana and Poplavskiy, Dmytro and Kalinin, Alexandr A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {1436-1443},
  doi       = {10.1109/CVPRW50498.2020.00183},
  url       = {https://mlanthology.org/cvprw/2020/gabruseva2020cvprw-deep/}
}