ArticleJournal of medical systems2012
Discovering mammography-based machine learning classifiers for breast cancer diagnosis.
Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
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- Interpretable Whole-Breast Radiomic Biomarkers for Exploratory Assessment of HER2 + Breast Cancer in Digital Mammography.Journal of imaging informatics in medicine · 2026Article
- Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.Journal of imaging informatics in medicine · 2024Article
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
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- Automatic disease prediction from human gut metagenomic data using boosting GraphSAGE.BMC bioinformatics · 2023Article
- Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records.Diagnostics (Basel, Switzerland) · 2023Article
- Evaluation of artificial intelligence techniques in disease diagnosis and prediction.Discover artificial intelligence · 2023Review
- A divide and conquer approach to maximise deep learning mammography classification accuracies.PloS one · 2023Article
- Article
- Classification of Breast Cancer Images by Implementing Improved DCNN with Artificial Fish School Model.Computational intelligence and neuroscience · 2022Article
- Lateral elbow tendinopathy and artificial intelligence: Binary and multilabel findings detection using machine learning algorithms.Frontiers in medicine · 2022Article
- Review
- Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease.Hypertension (Dallas, Tex. : 1979) · 2020Article
- Machine Learning in Oncology: Methods, Applications, and Challenges.JCO clinical cancer informatics · 2020Article
- Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review.Journal of medical radiation sciences · 2020Review
- A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis.BMC bioinformatics · 2020Article
- Statistical tools used for analyses of frequent users of emergency department: a scoping review.BMJ open · 2019Article
- Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.
Indexed as
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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.