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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

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