Evaluating ResNet-Based Self-Explanatory Models for Breast Lesion Classification

Adél Bajcsi, Camelia Chira, Annamária Szenkovits

2025

Abstract

Breast cancer is one of the leading causes of mortality among women diagnosed with cancer. In recent years, numerous computer-aided diagnosis (CAD) systems have been proposed for the classification of breast lesions. This study investigates self-explanatory deep learning models, namely BagNet and ProtoPNet, for the classification of breast abnormalities. Our aim is to train models to distinguish between benign and malignant lesions in breast tissue using publicly available datasets, namely MIAS and DDSM. The study provides a comprehensive numerical comparison of the two self-explanatory models and their respective backbones, as well as a visual evaluation of model performance. The results indicate that, while the backbone (black-box model) exhibits slightly better performance, it does so at the expense of interpretability. Conversely, BagNet, despite being a simpler model, achieves results comparable to those of ProtoPNet. In addition, transfer learning and data augmentation techniques are employed to enhance the performance of the CAD system.

Paper Citation

in Harvard Style

Bajcsi A., Chira C. and Szenkovits A. (2025). Evaluating ResNet-Based Self-Explanatory Models for Breast Lesion Classification. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 288-295. DOI: 10.5220/0013121900003890

in Bibtex Style

@conference{icaart25,
author={Adél Bajcsi and Camelia Chira and Annamária Szenkovits},
title={Evaluating ResNet-Based Self-Explanatory Models for Breast Lesion Classification},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={288-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013121900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Evaluating ResNet-Based Self-Explanatory Models for Breast Lesion Classification
SN - 978-989-758-737-5
AU - Bajcsi A.
AU - Chira C.
AU - Szenkovits A.
PY - 2025
SP - 288
EP - 295
DO - 10.5220/0013121900003890
PB - SciTePress