Tman: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images

Abstract

Accurately diagnosing various types of breast lesions is critical for assessing breast cancer risk and predicting patient outcomes, which necessitates a fine-grained classification approach. While convolutional neural networks (CNNs) are predominantly employed in fine-grained classification tasks for breast lesions, they often struggle to effectively capture and model the intricate relationships between local and global features—an aspect that is vital for achieving high classification accuracy. Additionally, Color Doppler Flow Imaging (CDFI) and Strain Elastography (SE) are two important ultrasound imaging techniques widely used in the diagnosis of breast lesions. However, their specific contributions to fine-grained classification have not been thoroughly investigated. In this paper, we introduce a Triple Morphological Feature Attention Network (TMAN) designed to enhance fine-grained classification of breast ultrasound images. The TMAN architecture comprises three key modules: Local Margin Attention (LMA), Global Texture Attention (GTA), and Fusion Attention (FA), each focused on extracting distinct morphological features. Our approach is validated on a private dataset, where we also assess the impact of CDFI and SE on fine-grained classification outcomes. The findings reveal that while CDFI images significantly enhance the classification accuracy of malignant breast cancer subtypes, they have little effect on benign lesion, and SE shows minimal impact on the prediction of both benign lesion and malignant breast cancer subtypes.

Note:
Funding Information: This research is supported by the National Natural Science Foundation of China (No. 72001063); the Fundamental Research Funds for the Central Universities (No. JZ2023HGTB0282, PA2024GDGP0031).

Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Keywords: Breast Lesion, Ultrasound Imaging, Fine-Grained Classification, Attention Mechanism