ReviewCurrent oncology (Toronto, Ont.)2022
Applying Deep Learning for Breast Cancer Detection in Radiology.
Review in Current oncology (Toronto, Ont.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.European radiology · 2025Pooled it
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Employing transfer learning for breast cancer detection using deep learning models.PLOS digital health · 2025Article
- Mam-Incept-Net: a novel inception model for precise interpretation of mammography images.PeerJ. Computer science · 2025Article
- The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography.Journal of imaging · 2024Article
- CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.Tomography (Ann Arbor, Mich.) · 2024Article
- Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population.Breast cancer research : BCR · 2024Article
- Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours.Frontiers in medicine · 2024Article
- Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.Breast cancer research : BCR · 2023Review
- Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions.Cancers · 2023Review
- Artificial Intelligence for Cancer Detection-A Bibliometric Analysis and Avenues for Future Research.Current oncology (Toronto, Ont.) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows. We summarize deep learning methods, data availability and different screening methods for breast cancer including mammography, thermography, ultrasound and magnetic resonance imaging. In this review, we will explore deep learning in diagnostic breast imaging and describe the literature review. As a conclusion, we discuss some of the limitations and opportunities of integrating artificial intelligence into breast cancer clinical practice.
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.