Using Ensemble Models in the Histological Examination of Tissue Abnormalities

TL;DR

This paper explores the use of ensemble models to improve the automatic detection of tissue abnormalities in histological samples, aiming to reduce false negatives and enhance diagnostic accuracy.

Contribution

It introduces an ensemble modeling approach specifically designed for histological tissue analysis to address underdiagnosis issues.

Findings

Ensemble models reduce false negatives in tissue abnormality detection.

Improved diagnostic accuracy over single-model approaches.

Potential for clinical application in histopathology workflows.

Abstract

Classification models for the automatic detection of abnormalities on histological samples do exists, with an active debate on the cost associated with false negative diagnosis (underdiagnosis) and false positive diagnosis (overdiagnosis). Current models tend to underdiagnose, failing to recognize a potentially fatal disease. The objective of this study is to investigate the possibility of automatically identifying abnormalities in tissue samples through the use of an ensemble model on data generated by histological examination and to minimize the number of false negative cases.