ArticleJournal of medical imaging and radiation oncology2021
Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset.
Article in Journal of medical imaging and radiation oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets.Radiology. Artificial intelligence · 2023Article
- Vision-Transformer-Based Transfer Learning for Mammogram Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification.Cancers · 2022Article
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Authors and funding
4 authors.
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Abstract
introductionThis study aims to evaluate deep learning (DL)-based artificial intelligence (AI) techniques for detecting the presence of breast cancer on a digital mammogram image.
methodsWe evaluated several DL-based AI techniques that employ different approaches and backbone DL models and tested the effect on performance of using different data-processing strategies on a set of digital mammographic images with annotations of pathologically proven breast cancer.
resultsOur evaluation uses the area under curve (AUC) and accuracy (ACC) for performance measurement. The best evaluation result, based on 349 test cases (930 test images), was an AUC of 0.8979 [95% confidence interval (CI) 0.873, 0.923] and ACC of 0.8178 [95% CI 0.785, 0.850]. This was achieved by an AI technique that utilises a certain family of DL models, namely ResNet, as its backbone, combines the global features extracted from the whole mammogram and the local features extracted from the automatically detected cancer and non-cancer local regions in the whole image, and leverages background cropping and text removal, contrast adjustment and more training data.
conclusionDL-based AI techniques have shown promising results in retrospective studies for many medical image analysis applications. Our study demonstrates a significant opportunity to boost the performance of such techniques applied to breast cancer detection by exploring different types of approaches, backbone DL models and data-processing strategies. The promising results we have obtained suggest further development of AI reading services could transform breast cancer screening in the future.
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