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Unsupervised Deep Anomaly Detection in Chest Radiographs
The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial netwo...
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| Publicado en: | J Digit Imaging |
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| Autores principales: | , , , , , , , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Springer International Publishing
2021
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8289984/ https://ncbi.nlm.nih.gov/pubmed/33555397 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10278-020-00413-2 |
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