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EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography
We present an interpretable end-to-end computer-aided detection and diagnosis tool for pulmonary nodules on computed tomography (CT) using deep learning-based methods. The proposed network consists of a nodule detector and a nodule malignancy classifier. We used RetinaNet to train a nodule detector...
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| Publicat a: | Proc SPIE Int Soc Opt Eng |
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| Autors principals: | , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2020
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7325481/ https://ncbi.nlm.nih.gov/pubmed/32606487 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1117/12.2551220 |
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