Cargando…
Evaluating reproducibility of AI algorithms in digital pathology with DAPPER
Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred...
Autores principales: | Bizzego, Andrea, Bussola, Nicole, Chierici, Marco, Maggio, Valerio, Francescatto, Margherita, Cima, Luca, Cristoforetti, Marco, Jurman, Giuseppe, Furlanello, Cesare |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6467397/ https://www.ncbi.nlm.nih.gov/pubmed/30917113 http://dx.doi.org/10.1371/journal.pcbi.1006269 |
Ejemplares similares
-
Predictability of drug-induced liver injury by machine learning
por: Chierici, Marco, et al.
Publicado: (2020) -
Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling
por: Chierici, Marco, et al.
Publicado: (2020) -
Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma
por: Maggio, Valerio, et al.
Publicado: (2018) -
Multi-omics integration for neuroblastoma clinical endpoint prediction
por: Francescatto, Margherita, et al.
Publicado: (2018) -
Phylogenetic convolutional neural networks in metagenomics
por: Fioravanti, Diego, et al.
Publicado: (2018)