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SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images
High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphol...
Autores principales: | Zormpas-Petridis, Konstantinos, Noguera, Rosa, Ivankovic, Daniela Kolarevic, Roxanis, Ioannis, Jamin, Yann, Yuan, Yinyin |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855703/ https://www.ncbi.nlm.nih.gov/pubmed/33552964 http://dx.doi.org/10.3389/fonc.2020.586292 |
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