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Regular Paper

DG-CNN: Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering Beihang University, Beijing 100191, China
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Hangzhou 310051, China
Department of Diagnostic Ultrasound, National Cancer Center, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, TX 75080-3021, U.S.A.
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Abstract

Although using convolutional neural networks (CNNs) for computer-aided diagnosis (CAD) has made tremendous progress in the last few years, the small medical datasets remain to be the major bottleneck in this area. To address this problem, researchers start looking for information out of the medical datasets. Previous efforts mainly leverage information from natural images via transfer learning. More recent research work focuses on integrating knowledge from medical practitioners, either letting networks resemble how practitioners are trained, how they view images, or using extra annotations. In this paper, we propose a scheme named Domain Guided-CNN (DG-CNN) to incorporate the margin information, a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound (BUS) images. In DG-CNN, attention maps that highlight margin areas of tumors are first generated, and then incorporated via different approaches into the networks. We have tested the performance of DG-CNN on our own dataset (including 1485 ultrasound images) and on a public dataset. The results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their performance. For example, experimental results on our dataset show that with a certain integrating mode, the improvement of using DG-CNN over a baseline network structure ResNet18 is 2.17% in accuracy, 1.69% in sensitivity, 2.64% in specificity and 2.57% in AUC (Area Under Curve). To the best of our knowledge, this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

Keywords

medical consensusdomain knowledgebreast cancer diagnosismargin mapdeep neural network

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Journal of Computer Science and Technology
Volume 37 Issue 2,
March 2022
Pages 277-294
DOI: 10.1007/s11390-020-0192-0
Cite this article:
Xie X-Z, Niu J-W, Liu X-F, et al. DG-CNN: Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images. Journal of Computer Science and Technology, 2022, 37(2): 277-294. https://doi.org/10.1007/s11390-020-0192-0

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Received: 26 November 2019
Accepted: 02 June 2020
Published: 31 March 2022
©Institute of Computing Technology, Chinese Academy of Sciences 2022
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