ArticleJournal of medical imaging (Bellingham, Wash.)2017
Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 58 citations in OpenAlex.
- MuSTAF: Clinically Relevant Multi-task Spatiotemporal Attention Fusion Framework for Breast Cancer Detection with Longitudinal Mammography.medRxiv : the preprint server for health sciences · 2026Article
- Hybrid transformer-based model for mammogram classification by integrating prior and current images.Medical physics · 2025Article
- Effective SMOTE boost with deep learning for IDC identification in whole-slide images.PloS one · 2025Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Review
- Robustness of Deep Networks for Mammography: Replication Across Public Datasets.Journal of imaging informatics in medicine · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.Scientific reports · 2023Article
- Artificial Intelligence in Breast X-Ray Imaging.Seminars in ultrasound, CT, and MR · 2023Review
- Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images.Computational and structural biotechnology journal · 2023Article
- A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms.Tomography (Ann Arbor, Mich.) · 2022Review
- Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.Pattern recognition · 2022Article
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
- Study on the Prediction Method of Long-term Benign and Malignant Pulmonary Lesions Based on LSTM.Frontiers in bioengineering and biotechnology · 2022Article
- Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms.IEEE journal of translational engineering in health and medicine · 2022Article
- An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.Medical image analysis · 2021Article
- Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables.Journal of biophotonics · 2020Article
- Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.IEEE transactions on medical imaging · 2020Article
- Stand-alone artificial intelligence - The future of breast cancer screening?Breast (Edinburgh, Scotland) · 2020Article
- Deep convolutional neural networks for mammography: advances, challenges and applications.BMC bioinformatics · 2019Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigate the addition of symmetry and temporal context information to a deep convolutional neural network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the location of a mass candidate and maps it to either the contralateral or prior mammogram, and regions of interest (ROIs) are extracted around each location. Two different architectures are subsequently explored: (1) a fusion model employing two datastreams where both ROIs are fed to the network during training and testing and (2) a stagewise approach where a single ROI CNN is trained on the primary image and subsequently used as a feature extractor for both primary and contralateral or prior ROIs. A "shallow" gradient boosted tree classifier is then trained on the concatenation of these features and used to classify the joint representation. The baseline yielded an AUC of 0.87 with confidence interval [0.853, 0.893]. For the analysis of symmetrical differences, the first architecture where both primary and contralateral patches are presented during training obtained an AUC of 0.895 with confidence interval [0.877, 0.913], and the second architecture where a new classifier is retrained on the concatenation an AUC of 0.88 with confidence interval [0.859, 0.9]. We found a significant difference between the first architecture and the baseline at high specificity with [Formula: see text]. When using the same architectures to analyze temporal change, we yielded an AUC of 0.884 with confidence interval [0.865, 0.902] for the first architecture and an AUC of 0.879 with confidence interval [0.858, 0.898] in the second setting. Although improvements for temporal analysis were consistent, they were not found to be significant. The results show our proposed method is promising and we suspect performance can greatly be improved when more temporal data become available.
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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.