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

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

SUBMITTER: Misra S 

PROVIDER: S-EPMC10658476 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature