Multi-Task Learning with Context-Oriented Self-Attention for Breast Ultrasound Image Classification and Segmentation

Meng Xu, Kuan Huang, Xiaojun Qi

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

18 Scopus citations

Abstract

Breast cancer is a great threat to women's health. Automatic analysis of Breast UltraSound (BUS) images can help radiologists make more accurate and efficient diagnoses of breast cancer. We propose a Multi-Task Learning Network with Context-Oriented Self-Attention (MTL-COSA) module to automatically and simultaneously segment tumors and classify them as benign or malignant. The COSA module incorporates prior medical knowledge to guide the network to learn contextual relationships for better feature representations in BUS images. Extensive cross-validation experiments are conducted on two public datasets to evaluate the performance of MTL-COSA and several state-of-the-art methods. MTL-COSA achieves the best classification results and second-best segmentation results compared with deep learning-based methods (5 classification methods and 3 segmentation methods).

Original languageEnglish
Title of host publicationISBI 2022 - Proceedings
Subtitle of host publication2022 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
ISBN (Electronic)9781665429238
DOIs
StatePublished - 2022
Event19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: 28 Mar 202231 Mar 2022

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2022-March
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Country/TerritoryIndia
CityKolkata
Period28/03/2231/03/22

Keywords

  • breast ultrasound
  • classification
  • context-oriented self-attention
  • multi-task learning
  • segmentation

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