ArticleComputational and mathematical methods in medicine2019
Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms.
Article in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.European radiology · 2021Trial
- Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant-Benign Discrimination.Tomography (Ann Arbor, Mich.) · 2026Article
- Mammo-AGE: deep learning estimation of breast age from mammograms.Nature communications · 2025Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Exploring the potential of machine learning in gynecological care: a review.Archives of gynecology and obstetrics · 2024Review
- Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours.Frontiers in medicine · 2024Article
- Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework.PeerJ. Computer science · 2024Article
- Deep learning performance for detection and classification of microcalcifications on mammography.European radiology experimental · 2023Article
- Can AI Reduce the Harms of Screening Mammography?Radiology. Artificial intelligence · 2023Article
- Artificial intelligence in breast imaging: Current situation and clinical challenges.Exploration (Beijing, China) · 2023Review
- A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.Journal of cancer research and clinical oncology · 2023Article
- A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer.International journal of molecular sciences · 2023Review
- An Online Mammography Database with Biopsy Confirmed Types.Scientific data · 2023Article
- Physical imaging parameter variation drives domain shift.Scientific reports · 2022Article
- A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation.Journal of digital imaging · 2022Article
- A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.La Radiologia medica · 2022Review
- Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Article
- End-to-End Calcification Distribution Pattern Recognition for Mammograms: An Interpretable Approach with GNN.Diagnostics (Basel, Switzerland) · 2022Article
- Digital breast tomosynthesis-based peritumoral radiomics approaches in the differentiation of benign and malignant breast lesions.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
- Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis.Frontiers in public health · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mammography is successfully used as an effective screening tool for cancer diagnosis. A calcification cluster on mammography is a primary sign of cancer. Early researches have proved the diagnostic value of the calcification, yet their performance is highly dependent on handcrafted image descriptors. Characterizing the calcification mammography in an automatic and robust way remains a challenge. In this paper, the calcification was characterized by descriptors obtained from deep learning and handcrafted descriptors. We compared the performances of different image feature sets on digital mammograms. The feature sets included the deep features alone, the handcrafted features, their combination, and the filtered deep features. Experimental results have demonstrated that the deep features outperform handcrafted features, but the handcrafted features can provide complementary information for deep features. We achieved a classification precision of 89.32% and sensitivity of 86.89% using the filtered deep features, which is the best performance among all the feature sets.
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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.