Deep learning-based multimodal fusion network for segmentation and classification of breast cancers using B-mode and elastography ultrasound images

Sampa Misra, Chiho Yoon, Kwang Ju Kim, Ravi Managuli, Richard G. Barr, Jongduk Baek, Chulhong Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Ultrasonography is one of the key medical imaging modalities for evaluating breast lesions. For differentiating benign from malignant lesions, computer-aided diagnosis (CAD) systems have greatly assisted radiologists by automatically segmenting and identifying features of lesions. Here, we present deep learning (DL)-based methods to segment the lesions and then classify benign from malignant, utilizing both B-mode and strain elastography (SE-mode) images. We propose a weighted multimodal U-Net (W-MM-U-Net) model for segmenting lesions where optimum weight is assigned on different imaging modalities using a weighted-skip connection method to emphasize its importance. We design a multimodal fusion framework (MFF) on cropped B-mode and SE-mode ultrasound (US) lesion images to classify benign and malignant lesions. The MFF consists of an integrated feature network (IFN) and a decision network (DN). Unlike other recent fusion methods, the proposed MFF method can simultaneously learn complementary information from convolutional neural networks (CNNs) trained using B-mode and SE-mode US images. The features from the CNNs are ensembled using the multimodal EmbraceNet model and DN classifies the images using those features. The experimental results (sensitivity of 100 ± 0.00% and specificity of 94.28 ± 7.00%) on the real-world clinical data showed that the proposed method outperforms the existing single- and multimodal methods. The proposed method predicts seven benign patients as benign three times out of five trials and six malignant patients as malignant five out of five trials. The proposed method would potentially enhance the classification accuracy of radiologists for breast cancer detection in US images.

Original languageEnglish
Article numbere10480
JournalBioengineering and Translational Medicine
Volume8
Issue number6
DOIs
Publication statusPublished - 2023 Nov

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Biomedical Engineering
  • Pharmaceutical Science

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