A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images

Jignesh Chowdary, Pratheepan Yogarajah, Priyanka Chaurasiaa, Velmathi Guriviah

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)
174 Downloads (Pure)

Abstract

Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by 1.08%, 4.13%, and classification by 1.16%, 2.34%, respectively than the methods available in the literature.
Original languageEnglish
Pages (from-to)3-12
Number of pages10
JournalUltrasonic Imaging
Volume44
Issue number1
DOIs
Publication statusPublished (in print/issue) - Jan 2022

Bibliographical note

Publisher Copyright:
© The Author(s) 2022.

Keywords

  • Multi-task learning
  • breast cancer
  • malignant
  • classification
  • segmentation
  • benign
  • multi-task learning

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