Joint 2D-3D Breast Cancer Classification [article]

Gongbo Liang, Xiaoqin Wang, Yu Zhang, Xin Xing, Hunter Blanton, Tawfiq Salem, Nathan Jacobs
2020 arXiv   pre-print
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
more » ... e, 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.
arXiv:2002.12392v1 fatcat:vm7en2gurrforapvd7y6i2lhvu