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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/2009| Deep Learning COVID-19 Features on CXR using Limited Training Data Sets | |
| Yujin Oh. Sangjoon Park. Jong Chul Ye. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2004.05758v1.pdf | |
| Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of the CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2004.05758v1.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| 1100892.pdf | 2.45 MB | Adobe PDF | Visualizar/Abrir |