TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images.
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 fo...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 1; pp. 82 - 103 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191694171&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191694171 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2026 vid: 39 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191694171 191694171 191694171 10.1007/s10278-025-01496-5 191694171 ppf: 82 ppct: 21 formats: tig: atl: TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images. aug: au: Wang, Dongyue Xue, Min Wang, Hui affil: https://ror.org/02czkny70 School of Management, Hefei University of Technology, Box 270, 230009, Hefei, Anhui, China sug: subj: Breast Diseases Ultrasonography Image Processing, Computer Assisted Methods Breast Diseases Classification Sensitivity and Specificity Evaluation Ultrasonography Methods Diagnosis, Computer Assisted Human Female Convolutional Neural Networks Precision Classification Algorithms Magnetic Resonance Imaging Histology Logistic Regression Odds Ratio Boosting Machine Learning Algorithms Detection Algorithms Radiomics DICOM Cysts Ultrasonography Fibrocystic Disease of Breast Ultrasonography Papilloma Ultrasonography Neoplasms, Fibrous Tissue Ultrasonography Breast Neoplasms Ultrasonography Breast Neoplasms Classification Validation Studies Descriptive Statistics Data Analysis Software Comparative Studies Image Interpretation, Computer Assisted Ultrasonography, Doppler, Color Information Science Deep Learning ROC Curve Reproducibility of Results Female ab: 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), Structured Texture Attention (STA), and Fusion Attention (FA), each focused on extracting distinct morphological features. TMAN achieved an average accuracy of 74.40%, precision of 73.18%, and specificity of 96.02%, surpassing state-of-the-art methods. The findings reveal that incorporating CDFI significantly improved classification for malignant subtypes with a 10% accuracy boost, while SE had a negligible impact. These findings highlight the effectiveness of TMAN in extracting nuanced morphological features and advancing precision in breast ultrasound diagnosis. The source code is accessible at https://github.com/windywindyw/TMAN. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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