ArticleScientific reports2025
A deep learning-based multimodal medical imaging model for breast cancer screening.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Foundation model-powered deep learning of endometrial histology for predicting the cumulative live birth of an in vitro fertilization cycle.PLOS digital health · 2026Article
- An Efficient Cross-Modal Interaction and Dynamic Fusion Network for Multimodal Breast Ultrasound Diagnosis.Tomography (Ann Arbor, Mich.) · 2026Article
- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- A deep learning system for non-invasive breast cancer diagnosis with multimodal data.Nature biomedical engineering · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- 3D Medical Image Segmentation with 3D Modelling.Bioengineering (Basel, Switzerland) · 2026Article
- Non-Invasive Breast Cancer Receptor Typing from Mammograms Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- FGOOPNet: A Fuzzy Deep Learning Approach for Mammographic Breast Cancer ClassificationCurrent medical imaging · 2026Article
- Emerging Targeted and Multimodal Therapeutic Strategies in Breast Cancer: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- SwinCAMF-Net: Explainable Cross-Attention Multimodal Swin Network for Mammogram Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- HyFusion-X: hybrid deep and traditional feature fusion with ensemble classifiers for breast cancer detection using mammogram and ultrasound images.Scientific reports · 2025Article
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 2025Review
- The Evolving Landscape of Deep Learning in Breast Cancer Imaging: A Bibliometric Study of Segmentation, Detection, and Diagnostic Research From 2006 to 2025.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
In existing breast cancer prediction research, most models rely solely on a single type of imaging data, which limits their performance. To overcome this limitation, the present study explores breast cancer prediction models based on multimodal medical images (mammography and ultrasound images) and compares them with single-modal models. We collected medical imaging data from 790 patients, including 2,235 mammography images and 1,348 ultrasound images, and conducted a comparison using six deep learning classification models to identify the best model for constructing the multimodal classification model. Performance was evaluated using metrics such as area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and accuracy to compare the multimodal and single-modal classification models. Experimental results demonstrate that the multimodal classification model outperforms single-modal models in terms of specificity (96.41% (95% CI:93.10%-99.72%)), accuracy (93.78% (95% CI:87.67%-99.89%)), precision (83.66% (95% CI:76.27%-91.05%)), and AUC (0.968 (95% CI:0.947-0.989)), while single-modal models excel in sensitivity. Additionally, heatmap visualization was used to further validate the classification performance of the multimodal model. In conclusion, our multimodal classification model shows strong potential in breast cancer screening tasks, effectively assisting physicians in improving screening accuracy.
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Registered trials
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 OpenQuestion graph.