Deep Learning for Whole Slide Image Analysis: An Overview

Frontiers in Medicine

article

Neofytos Dimitriou¹  Ognjen Arandjelović¹  Peter D. Caie² 

[1] School of Computer Science, University of St Andrews, United Kingdom;School of Medicine, University of St Andrews, United Kingdom

关键词: digital pathology;    computer vision;    oncology;    cancer;    machine learning;    personalized pathology;    image analysis;   

DOI  :  10.3389/fmed.2019.00264

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.

【 授权许可】

CC BY