Deep Learning for Whole Slide Image Analysis: An Overview

TL;DR

This paper reviews how deep learning techniques are applied to analyze gigapixel whole slide images in pathology, addressing challenges like high heterogeneity and artefacts to enable clinical use.

Contribution

It provides a comprehensive overview of current deep learning methods for whole slide image analysis and discusses key challenges and solutions in the field.

Findings

Deep learning significantly improves visual understanding of whole slide images.

Challenges include handling billions of pixels, heterogeneity, and artefacts.

Addressing these challenges is crucial for clinical translation.

Abstract

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 artefacts. 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.