Comparative Analysis of Learning Strategies for Multi-Magnification Pathological Image Classification

Yixuan Pu

2025

Abstract

The automatic classification of pathology images plays a crucial role in computer-aided diagnosis by enhancing diagnostic efficiency and minimizing human error. In this paper, the Enteroscope Biopsy Histopathological H&E Image Dataset (EBHI) is utilized to systematically compare and analyze the performance of three strategies—Single-Magnification Training, Multi-Channel Fusion, and Stepwise Cumulative Learning—to optimize pathology image classification. The Single-Magnification Training strategy serves as a baseline experiment to validate the optimization effect of the model, achieving the highest classification accuracy of 94.64% at 200× magnification. Under strict filtering conditions, Multi-Channel Fusion achieves a peak classification accuracy of 96.06%. However, this approach remains inferior to Stepwise Cumulative Learning. This learning strategy significantly outperforms training solely at the highest magnification, achieving a classification accuracy of 98.27% on 400× images. This study demonstrates that the cumulative learning strategy effectively enhances the classification performance of pathology images. Low-magnification images contribute to improving the classification accuracy of high-magnification images, offering new insights into multi-scale feature fusion, dynamic learning strategies, and computer-aided pathology diagnosis. Furthermore, this study validates the applicability of the EBHI dataset in multi-magnification pathology analysis and advances the development of intelligent pathology image analysis.

Paper Citation

in Harvard Style

Pu Y. (2025). Comparative Analysis of Learning Strategies for Multi-Magnification Pathological Image Classification. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 222-229. DOI: 10.5220/0013681400004670

in Bibtex Style

@conference{icdse25,
author={Yixuan Pu},
title={Comparative Analysis of Learning Strategies for Multi-Magnification Pathological Image Classification},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={222-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013681400004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Comparative Analysis of Learning Strategies for Multi-Magnification Pathological Image Classification
SN - 978-989-758-765-8
AU - Pu Y.
PY - 2025
SP - 222
EP - 229
DO - 10.5220/0013681400004670
PB - SciTePress