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Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis

  • Jiali Liu
  • Wanyu Li
  • Ningbo Zhao
  • Kunlin CaoEmail author
  • Youbing YinEmail author
  • Qi Song
  • Hanbo Chen
  • Xuehao GongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Breast cancer is the most common cancer in women, and ultrasound imaging is one of the most widely used approach for diagnosis. In this paper, we proposed to adopt Convolutional Neural Network (CNN) to classify ultrasound images and predict tumor malignancy. CNN is a successful algorithm for image recognition tasks and has achieved human-level performance in real applications. To improve the performance of CNN in breast cancer diagnosis, we integrated domain knowledge and conducted multi-task learning in the training process. After training, a radiologist visually inspected the class activation map of the last convolutional layer of trained network to evaluate the result. Our result showed that CNN classifier can not only give reasonable performance in predicting breast cancer, but also propose potential lesion regions which can be integrated into the breast ultrasound system in the future.

Keywords

Breast cancer Ultrasound BI-RADS assessments Convolutional neural network Multi-task learning 

Notes

Acknowledgement

The work received supports from Shenzhen Municipal Government under the grants JCYJ20170413161913429 and KQTD2016112809330877.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Shenzhen Second People’s HospitalShenzhenChina
  2. 2.Anhui Medical UniversityHeifeiChina
  3. 3.Shenzhen Keya Medical Technology CorporationShenzhenChina
  4. 4.The Third People’s Hospital of ShenzhenShenzhenChina

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