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

Xin LiGenggeng QinQiang HeLei SunHui ZengZilong HeWeiguo ChenXin ZhenLinghong Zhou
Published in: European radiology (2019)
• 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.
Keyphrases
  • deep learning
  • machine learning
  • artificial intelligence
  • convolutional neural network
  • high intensity
  • magnetic resonance imaging
  • image quality
  • electron transfer
  • contrast enhanced