Deep Learning for Automatic Pneumonia Detection
This work addresses a critical medical issue for global health by potentially enhancing diagnostic efficiency, though it appears incremental as it builds on existing deep learning methods for a specific challenge.
The paper tackled the problem of automating pneumonia detection from chest X-rays to reduce reliance on specialists and improve diagnostic accuracy, achieving one of the best results in the Radiological Society of North America Pneumonia Detection Challenge.
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.