Joint Localization and Classification of Breast Cancer in B-Mode Ultrasound Imaging via Collaborative Learning With Elastography

Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performin...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2022-09, Vol.26 (9), p.4474-4485
Main Authors: Ding, Weichang, Wang, Jun, Zhou, Weijun, Zhou, Shichong, Chang, Cai, Shi, Jun
Format: Article
Language:English
Subjects:
Artificial neural networks
Breast cancer
Cams
Classification
Collaborative learning
collaborative learning with multi-scale consistency
Convolutional neural networks
Deep learning
Diagnosis
Learning
Localization
Location awareness
Network management systems
Neural networks
Optimization
Stiffness
tumor classification
tumor localization
Tumors
Ultrasonic imaging
Ultrasound
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Summary:Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performing them simultaneously. Besides, they suffer from the limited diagnosis information in the B-mode ultrasound (BUS) images. In this study, we develop a novel network ResNet-GAP that incorporates both localization and classification into a unified procedure. To enhance the performance of ResNet-GAP, we leverage stiffness information in the elastography ultrasound (EUS) modality by collaborative learning in the training stage. Specifically, a dual-channel ResNet-GAP network is developed, one channel for BUS and the other for EUS. In each channel, multiple class activity maps (CAMs) are generated using a series of convolutional kernels of different sizes. The multi-scale consistency of the CAMs in both channels are further considered in network optimization. Experiments on 264 patients in this study show that the newly developed ResNet-GAP achieves an accuracy of 88.6%, a sensitivity of 95.3%, a specificity of 84.6%, and an AUC of 93.6% on the classification task, and a 1.0NLF of 87.9% on the localization task, which is better than some state-of-the-art approaches.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2022.3186933