Cargando…
Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise
Autores principales: | Driggs, Derek, Selby, Ian, Roberts, Michael, Gkrania-Klotsas, Effrossyni, Rudd, James H. F., Yang, Guang, Babar, Judith, Sala, Evis, Schönlieb, Carola-Bibiane |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Radiological Society of North America
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995449/ https://www.ncbi.nlm.nih.gov/pubmed/34240059 http://dx.doi.org/10.1148/ryai.2021210011 |
Ejemplares similares
-
A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data
por: Breger, Anna, et al.
Publicado: (2023) -
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
por: Yeung, Michael, et al.
Publicado: (2021) -
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
por: Yeung, Michael, et al.
Publicado: (2022) -
Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation
por: Yeung, Michael, et al.
Publicado: (2022) -
Herpes simplex 1 encephalitis presenting as a brain haemorrhage with normal cerebrospinal fluid analysis: a case report
por: Gkrania-Klotsas, Effrossyni, et al.
Publicado: (2008)