TY - GEN
T1 - SHAPE-ADAPTIVE CONVOLUTIONAL OPERATOR FOR BREAST ULTRASOUND IMAGE SEGMENTATION
AU - Huang, Kuan
AU - Zhang, Yingtao
AU - Cheng, H. D.
AU - Xing, Ping
N1 - Publisher Copyright:
© 2021 IEEE Computer Society. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Convolutional neural networks (CNNs) are widely used in medical image analysis, especially for breast ultrasound (BUS) image segmentation. Automatically encoding deep features is one of the most important reasons leading to the success of deep convolutional neural networks. There are a lot of studies on obtaining better convolutional features; however, they do not discuss the higher-order information in the features. In this research, we propose a novel convolutional operator, a shape-adaptive convolutional operator, which can select pixels for calculating convolution rather than in the Euclidean space. The proposed operator is combined with the original convolutional operator to extract higher-order convolutional features. We conduct extensive experiments to evaluate the performance of the proposed operator for image segmentation using three datasets: two public BUS image datasets and one multi-category BUS image dataset. The proposed approach achieves state-of-the-art performance.
AB - Convolutional neural networks (CNNs) are widely used in medical image analysis, especially for breast ultrasound (BUS) image segmentation. Automatically encoding deep features is one of the most important reasons leading to the success of deep convolutional neural networks. There are a lot of studies on obtaining better convolutional features; however, they do not discuss the higher-order information in the features. In this research, we propose a novel convolutional operator, a shape-adaptive convolutional operator, which can select pixels for calculating convolution rather than in the Euclidean space. The proposed operator is combined with the original convolutional operator to extract higher-order convolutional features. We conduct extensive experiments to evaluate the performance of the proposed operator for image segmentation using three datasets: two public BUS image datasets and one multi-category BUS image dataset. The proposed approach achieves state-of-the-art performance.
KW - breast ultrasound (BUS) image
KW - higher-order information
KW - semantic segmentation
KW - shape-adaptive convolution
UR - http://www.scopus.com/inward/record.url?scp=85122995374&partnerID=8YFLogxK
U2 - 10.1109/ICME51207.2021.9428287
DO - 10.1109/ICME51207.2021.9428287
M3 - Conference contribution
AN - SCOPUS:85122995374
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2021 IEEE International Conference on Multimedia and Expo, ICME 2021
PB - IEEE Computer Society
T2 - 2021 IEEE International Conference on Multimedia and Expo, ICME 2021
Y2 - 5 July 2021 through 9 July 2021
ER -