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The uncertainty of boundary can improve the classification accuracy of BI-RADS 4A ultrasound image.

Huayu WangYixin HuYao LuJian-Hua ZhouYongze Guo
Published in: Medical physics (2022)
Using the uncertain boundaries defined by the voting methods as auxiliary information, we obtained a better performance in the classification of BI-RADS 4A ultrasound images, while variance-based uncertain boundaries had no effect on improving classification performance. Additionally, fine-grained network helped find discriminative features comparing with the commonly used classification networks.
Keyphrases
  • deep learning
  • machine learning
  • convolutional neural network
  • magnetic resonance imaging
  • air pollution
  • computed tomography
  • health information
  • contrast enhanced ultrasound