SHAPE-ADAPTIVE CONVOLUTIONAL OPERATOR FOR BREAST ULTRASOUND IMAGE SEGMENTATION

Kuan Huang, Yingtao Zhang, H. D. Cheng, Ping Xing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Multimedia and Expo, ICME 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665438643
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, China
Duration: 5 Jul 20219 Jul 2021

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2021 IEEE International Conference on Multimedia and Expo, ICME 2021
Country/TerritoryChina
CityShenzhen
Period5/07/219/07/21

Keywords

  • breast ultrasound (BUS) image
  • higher-order information
  • semantic segmentation
  • shape-adaptive convolution

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