Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

The authors of this study evaluated the impact of utilizing digital breast tomosynthesis (DBT) and/or full-field digital mammography (FFDM), and different transfer learning strategies, on deep convolutional neural network (DCNN)-based mass classification for breast cancer. The authors found that integrating DBT and FFDM in DCNN training helps to enhance breast mass classification accuracy.

Key points

Transfer learning facilitates mass classification for both DBT and FFDM, and the DBT-based DCNN outperforms the FFDM-based DCNN when equipped with transfer learning.
Integrating DBT and FFDM in DCNN training enhances breast mass classification accuracy.
3D-DCNN/2D-DCNN trained from scratch with volumetric DBT but without transfer learning only produce moderate mass classification result.

Article: Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

Authors: Xin Li, Genggeng Qin, Qiang He, Lei Sun, Hui Zeng, Zilong He, Weiguo Chen, Xin Zhen & Linghong Zhou