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Related Experiment Video

Updated: Jul 24, 2025

Automating Aggregate Quantification in Caenorhabditis elegans

Automating Aggregate Quantification in Caenorhabditis elegans

Published on: October 14, 2021

An aggregation of aggregation methods in computational pathology.

Mohsin Bilal¹, Robert Jewsbury², Ruoyu Wang²

¹Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK; School of Computing, National University of Computer and Emerging Sciences, Islamabad, Pakistan.

Medical Image Analysis

|July 9, 2023

Summary

This summary is machine-generated.

This review explores aggregation methods for whole-slide image (WSI) analysis in computational pathology (CPath). It offers a framework and comparisons to guide future WSI-level predictive modeling research.

Keywords:

Aggregation of predictions Computational pathology Machine learning Whole slide image analysis

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Area of Science:

Digital pathology
Computational pathology (CPath)
Machine learning in medicine

Background:

Whole-slide images (WSIs) require aggregation of tile-level predictions for WSI-level analysis.
Existing aggregation methods in computational pathology need systematic review and comparison.

Purpose of the Study:

To review and categorize aggregation methods for WSI analysis in CPath.
To propose a general CPath workflow for predictive modeling.
To guide future research in WSI-level prediction.

Main Methods:

Literature review of aggregation methods for WSI analysis.
Categorization of methods based on data context, computational modules, and CPath use cases.
Comparison of methods, particularly multiple instance learning, on a specific WSI-level prediction task.

Main Results:

A proposed general CPath workflow with three pathways.
Categorization and comparison of various aggregation techniques.
Identification of objectives, desirable attributes, pros, and cons of different aggregation approaches.

Conclusions:

Aggregation methods are crucial for WSI-level prediction in CPath.
A structured approach and fair comparison are needed to advance the field.
Recommendations and future research directions are provided for CPath aggregation methods.