SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images

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...

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Bibliographic Details
Main Authors: Konstantinos Zormpas-Petridis, Rosa Noguera, Daniela Kolarevic Ivankovic, Ioannis Roxanis, Yann Jamin, Yinyin Yuan
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Oncology
Subjects:
deep learning
machine learning
digital pathology
computational pathology
tumor region classification
melanoma
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2020.586292/full

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