ReviewJournal of imaging2022
AI in Breast Cancer Imaging: A Survey of Different Applications.
Review in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions.Journal of imaging · 2026Review
- A deep learning system for non-invasive breast cancer diagnosis with multimodal data.Nature biomedical engineering · 2026Article
- AI calls the bluff: differentiating benign lesions from triple-negative breast cancer cases.La Radiologia medica · 2026Article
- The impact of pre-processing techniques on deep learning breast image segmentation.Scientific reports · 2025Article
- When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer.Medical & biological engineering & computing · 2025Article
- Identifying ovarian cancer with machine learning DNA methylation pattern analysis.Scientific reports · 2025Article
- Deep learning in breast cancer risk prediction: a review of recent applications in full-field digital mammography.Frontiers in oncology · 2025Review
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models.Sensors (Basel, Switzerland) · 2024Article
- Application of Deep Learning System Technology in Identification of Women's Breast Cancer.Medicina (Kaunas, Lithuania) · 2023Article
- Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks.Tomography (Ann Arbor, Mich.) · 2023Article
- The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant-which could be important to diminish reading time and improve accuracy-are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms-which may be able to allow screening programs customization both on periodicity and modality-are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome.
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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.