Applying Multiple Instance Learning for Breast Cancer Lesion Detection in Mammography Images

Nedra Amara, Said Gattoufi

2024

Abstract

Breast cancer remains a major global health problem and early detection is essential to improve patient outcomes. Current computer-aided detection (CAD) systems for breast cancer are often based on fully supervised training, which requires careful manual annotation and accurate tumor segmentation. This paper presents a novel approach based on multiple instance and transfer learning techniques. Our method uses an adapted threshold segmentation technique to extract many small spots from mammography images. Instance features are then extracted using a pre-trained model and grouped into a unified representation. A classifier trained on these representations is used to classify the data. The proposed method eliminates the need for precise tumor segmentation while demonstrating high accuracy in breast cancer detection.

Paper Citation

in Harvard Style

Amara N. and Gattoufi S. (2024). Applying Multiple Instance Learning for Breast Cancer Lesion Detection in Mammography Images. In Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE; ISBN 978-989-758-700-9, SciTePress, pages 93-97. DOI: 10.5220/0012689500003699

in Bibtex Style

@conference{ict4awe24,
author={Nedra Amara and Said Gattoufi},
title={Applying Multiple Instance Learning for Breast Cancer Lesion Detection in Mammography Images},
booktitle={Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE},
year={2024},
pages={93-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012689500003699},
isbn={978-989-758-700-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE
TI - Applying Multiple Instance Learning for Breast Cancer Lesion Detection in Mammography Images
SN - 978-989-758-700-9
AU - Amara N.
AU - Gattoufi S.
PY - 2024
SP - 93
EP - 97
DO - 10.5220/0012689500003699
PB - SciTePress