Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.
@article{arxiv.2002.12392,
title = {Joint 2D-3D Breast Cancer Classification},
author = {Gongbo Liang and Xiaoqin Wang and Yu Zhang and Xin Xing and Hunter Blanton and Tawfiq Salem and Nathan Jacobs},
journal= {arXiv preprint arXiv:2002.12392},
year = {2020}
}
Comments
Accepted by IEEE International Conference of Bioinformatics and Biomedicine (BIBM), 2019