Abnormality Detection in Smartphone-Captured Chest Radiograph Using Multi-pretrained Models
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6547-2_7
Reference13 articles.
1. Rajpurkar P et al (2017) Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. Preprint at https://arxiv.org/abs/1711.05225
2. Tang Y-X et al (2020) Automated abnormality classification of chest radiographs using deep conolutional neural networks. npj Digit Med 3:70
3. Pham HH, Le TT, Tran DQ, Ngo DT, Nguyen HQ (2019) Interpreting chest X rays via CNNs that exploit disease dependencies and uncertainty labels. Preprint at https://arxiv.org/abs/1911.06475
4. Sabottke CF, Spieler BM (2020) The effect of image resolution on deep learning in radiography. Radiol Artif Intell 2:e190015
5. Taylor AG, Mielke C, Mongan J (2018) Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: a retrospective study. PLoS Med 15. https://doi.org/10.1371/journal.pmed.1002697
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