Pneumonia Detection in Chest Radiographs
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Abstract (arXiv)
In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only open-source deep learning object detection and is based on CoupleNet, a fully convolutional network which incorporates global and local features for object detection. Our approach achieves robustness through critical modifications of the training process and a novel ensembling algorithm which merges bounding boxes from several models. We tested our detection algorithm tested on a dataset of 3000 chest radiographs as part of the 2018 RSNA Pneumonia Challenge; our solution was recognized as a winning entry in a contest which attracted more than 1400 participants worldwide.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2018.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2022Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographscited by 12
- 2021MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysiscited by 6
- 2021Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysiscited by 0
- 2022Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs9 May 2022
- 2021Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis16 Nov 2021
- 2021MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis12 Oct 2021