ArticleJournal of applied clinical medical physics2026
Multimodal deep learning for breast tumor classification: Integrating mammography and ultrasound for enhanced diagnostic accuracy.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.Frontiers in molecular biosciences · 2026Review
- Multimodal deep learning for breast tumor classification: Integrating mammography and ultrasound for enhanced diagnostic accuracy.Journal of applied clinical medical physics · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundDeep learning has advanced breast tumor prediction research, but traditional single-modality models limit feature diversity and accuracy. PURPOSE: To develop and validate a multimodal deep learning approach that combines mammography and ultrasound imaging for improved breast tumor classification and enhanced clinical decision-making.
methodsThis retrospective study analyzed 663 female patients with breast lesions from 2018 to 2021, including 384 benign and 279 malignant cases. The two-stage prediction model employed improved modality-specific attention mechanisms: efficient channel attention (ECA-Net) for ultrasound and convolutional block attention module (CBAM) for mammography. The fused features were input into a stacking ensemble module with logistic regression (LR), support vector machine (SVM), random forest (RF), and Extra-Trees (ET) as base learners, and multilayer perceptron (MLP) neural network as meta-learner. Data was divided into training (464), validation (133), and test (66) sets with a 7:2:1 ratio.
resultsThe proposed multimodal prediction model-mammography ultrasound (MPM-MU) achieved superior performance with an area under the receiver operating characteristic (ROC) Curve (AUC) of 87.9 ± 0.21%, representing improvements of 13.4% and 15.6% over attention-enhanced mammography (74.5%) and ultrasound (72.3%) models, respectively. Ablation studies confirmed the effectiveness of both multimodal feature fusion and attention mechanisms in enhancing diagnostic performance.
conclusionsThe multimodal prediction model-mammography ultrasound (MPM-MU) with modality-specific attention mechanisms demonstrated superior performance in distinguishing between benign and malignant breast tumors compared to single-modality approaches. This approach assists radiologists in improving breast lesion classification accuracy and enhancing clinical decision-making, potentially reducing unnecessary biopsies and improving diagnostic consistency.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.