A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology

TL;DR

This paper reviews set-based aggregation methods for creating compact, single-vector representations of whole slide images in digital pathology, aiming to improve efficiency and accessibility in computational analysis.

Contribution

It provides a comprehensive overview of existing set-based techniques for WSI representation, emphasizing innovations that enhance computational efficiency and storage management.

Findings

Set-based methods improve WSI representation efficiency.

These techniques facilitate scalable search and retrieval in digital pathology.

They address storage limitations in healthcare settings.

Abstract

Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel files that capture the intricate details of tissue samples, providing a rich source of information for diagnostic and research purposes. However, due to their enormous size, representing these images as one compact vector is essential for many computational pathology tasks, such as search and retrieval, to ensure efficiency and scalability. Most current methods are "patch-oriented," meaning they divide WSIs into smaller patches for processing, which prevents a holistic analysis of the entire slide. Additionally, the necessity for compact representation is driven by the expensive high-performance storage required for WSIs. Not all hospitals have access to such extensive storage solutions, leading to potential disparities in healthcare quality and accessibility. This paper provides an overview of existing set-based approaches to single-vector WSI representation, highlighting the innovations that allow for more efficient and effective use of these complex images in digital pathology, thus addressing both computational challenges and storage limitations.