SynthesisJournal of medical Internet research2019
Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.
Synthesis in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 6 of them syntheses 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.
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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.
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Who cites it
61 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption.Journal of medical Internet research · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023Pooled it
- Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.JCO clinical cancer informatics · 2023Pooled it
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Predicting breast cancer 5-year survival using machine learning: A systematic review.PloS one · 2021Pooled it
- FT-MDNNMDs: early detection of breast cancer using fine-tuned multi-deep neural networks with TCGA and clinical image datasets.Scientific reports · 2026Article
- Breast cancer risk assessment for screening: a hybrid artificial intelligence approach.European radiology · 2026Article
- Prospective Multicenter Evaluation of Patient Satisfaction and Radiologist Image Interpretation Using the Envision™ Mammography Platform.Medical devices (Auckland, N.Z.) · 2026Article
- Multimodal feature fusion model for breast mass malignant risk stratification.Frontiers in oncology · 2026Article
- Monte Carlo Tree Search for optimal cancer intervention strategies among BRCA mutation carriers.Computers in biology and medicine · 2025Article
- Review
- Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice.Diagnostics (Basel, Switzerland) · 2025Review
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 2025Review
- Context-Based Human Influence and Causal Responsibility for Assisted Decision-Making.Human factors · 2025Article
- Employing transfer learning for breast cancer detection using deep learning models.PLOS digital health · 2025Article
- An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.Scientific reports · 2025Article
- Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights.Breast cancer research : BCR · 2025Article
- Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretability.PeerJ. Computer science · 2025Article
1 more citing papers are in PubMed but not listed here.
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
backgroundMachine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems.
objectiveThis review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks.
methodsIn this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets.
resultsThe analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems.
conclusionsFrom the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
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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.