ReviewFrontiers in oncology2024
Mammography with deep learning for breast cancer detection.
Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review and Meta-Analysis of AI-Assisted Mammography and the Systemic Immune-Inflammation Index in Breast Cancer: Diagnostic and Prognostic Perspectives.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Microwave breast imaging: A review of clinical potential and technological advances.World journal of methodology · 2026Review
- MMBCFNet: multi modal hybrid deep learning framework for breast cancer detection using MRI, mammography, and ultrasound images.Journal of ultrasound · 2026Article
- Study the Changing Trends of Age at Diagnosis of Breast Carcinoma in Correlation with Polycystic Ovarian Syndrome, Obesity, and Nutritional Status.Annals of African medicine · 2026Observational
- Addressing Clinical Ambiguity in Breast Density Assessment: A Hybrid Multi-View Deep Learning Framework for BI-RADS B vs. C Classification.Diagnostics (Basel, Switzerland) · 2026Article
- UBB as an early-stage potential biomarker for breast cancer via modulation of the ubiquitination pathway.Scientific reports · 2026Article
- Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026Article
- Monte Carlo validation of signal-to-noise improvements in transmission imaging using correlated annihilation photon pairs.Scientific reports · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Review
- Enhanced breast cancer detection framework based on YOLOv11n with multi-scale feature calibration.Scientific reports · 2026Article
- Massive breast fat necrosis with invasive breast cancer: A case report.Oncology letters · 2026Article
- AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.Frontiers in molecular biosciences · 2026Review
- Big Data Analytics Framework for Early Breast Cancer Detection Using Optimized MapReduce and Deep Learning on Large-Scale Mammographic Data.International journal of biomedical imaging · 2026Article
- Breast cancer pathogenesis, diagnosis and treatment: a comprehensive review.Frontiers in oncology · 2026Review
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Anatomy-guided breast segmentation in thermograms using a multiscale UNet hybrid framework.BMC medical imaging · 2025Article
- Explainable Deep Learning for Breast Lesion Classification in Digital and Contrast-Enhanced Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.
Indexed as
Identifiers
What OpenQuestion holds
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.