ReviewFrontiers in oncology2023
The application of traditional machine learning and deep learning techniques in mammography: a review.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis.MethodsX · 2026Article
- The effect of educational intervention based on the stages of precaution adoption process model and health belief model on mammography screening behavior.Journal of education and health promotion · 2026Article
- From Machine Learning to Ensemble Approaches: A Systematic Review of Mammogram Classification Methods.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2024Article
- Largescale multicenter study of a serum metabolite biomarker panel for the diagnosis of breast cancer.iScience · 2024Article
- Breast cancer screening in women taking hormone replacement therapy needs updating.Facts, views & vision in ObGyn · 2024Article
- Application of artificial intelligence in the diagnosis and treatment of urinary tumors.Frontiers in oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients' physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients' overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions.
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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.