MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework. (March 2023)

• Record Type: Journal Article
• Authors: Zhong, Shengzhou; Tu, Chao; Dong, Xiuyu; Feng, Qianjin; Chen, Wufan; Zhang, Yu
• Is Part Of: Computer methods and programs in biomedicine. Volume 230(2023)
• Publication Date: 2023-03
• Subjects: Multi-scale -- Gradational-order fusion -- Breast lesion classification -- Ultrasound image; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107346
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A multi-scale gradational-order fusion framework for predicting the malignant breast lesions. A spatial context aggregation module mines multi-scale context and preserves the long-range dependency of mono-scale image. Learning and combining multi-order representations for better characterizing complex textures and enhancing non-linear modeling capability. Extensive experiments demonstrate the effectiveness of the proposed method. Abstract: Background and Objective: Predicting the malignant potential of breast lesions based on breast ultrasound (BUS) images is a crucial component of computer-aided diagnosis system for breast cancers. However, since breast lesions in BUS images generally have various shapes with relatively low contrast and present complex textures, it still remains challenging to accurately identify the malignant potential of breast lesions. Methods: In this paper, we propose a multi-scale gradational-order fusion framework to make full advantages of multi-scale representations incorporating with gradational-order characteristics of BUS images for breast lesions classification. Specifically, we first construct a spatial context aggregation module to generate multi-scale context representations from the original BUS images. Subsequently, multi-scale representations are efficiently fused in feature fusion block that is armed with special fusion strategies to comprehensively capture morphological characteristics of breast lesions. To better characterize complex textures and enhance non-linear modeling capability, we further propose isotropous gradational-order feature module in the feature fusion block to learn and combine multi-order representations. Finally, these multi-scale gradational-order representations are utilized to perform prediction for the malignant potential of breast lesions. Results: The proposed model was evaluated on three open datasets by using 5-fold cross-validation. The experimental results (Accuracy: 85.32%, Sensitivity: 85.24%, Specificity: 88.57%, AUC: 90.63% on dataset A; Accuracy: 76.48%, Sensitivity: 72.45%, Specificity: 80.42%, AUC: 78.98% on dataset B) demonstrate that the proposed method achieves the promising performance when compared with other deep learning-based methods in BUS classification task. Conclusions: The proposed method has demonstrated a promising potential to predict malignant potential of breast lesion using ultrasound image in an end-to-end manner.