Deep Learning for Automatic Pneumonia Detection

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Authors Alexandr A. Kalinin, Tatiana Gabruseva, Dmytro Poplavskiy
Journal/Conference Name IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Paper Category
Paper 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-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.
Date of publication 2020
Code Programming Language Python
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