Deep Learning for Automatic Pneumonia Detection

• URL: http://arxiv.org/abs/2005.13899v1
• Date: Thu, 28 May 2020 10:54:34 GMT
• Title: Deep Learning for Automatic Pneumonia Detection
• Authors: Tatiana Gabruseva, Dmytro Poplavskiy, Alexandr A. Kalinin
• Abstract summary: Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. 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.
• Score: 72.55423549641714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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