ArticleIEEE transactions on medical imaging2022
Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 35 citations in OpenAlex.
- Mammo-GAN-Assisted Deep Network Training Scheme for Lesion Detection.Journal of imaging informatics in medicine · 2025Article
- Impact of synthetic data on training a deep learning model for lesion detection and classification in contrast-enhanced mammography.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Occult breast cancer in an older woman: A case report.Experimental and therapeutic medicine · 2025Article
- Intelligent breast cancer diagnosis with two-stage using mammogram images.Scientific reports · 2024Article
- Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover.Breast cancer research : BCR · 2024Article
- Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Developing breast lesion detection algorithms for digital breast tomosynthesis: Leveraging false positive findings.Medical physics · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Our objective is to show the feasibility of using simulated mammograms to detect mammographically-occult (MO) cancer in women with dense breasts and a normal screening mammogram who could be triaged for additional screening with magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional Generative Adversarial Network (CGAN) to simulate a mammogram with normal appearance using the opposite mammogram as the condition. We used a Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN, we used screening mammograms of 1366 women. For MO cancer detection, we used screening mammograms of 333 women (97 MO cancer) with dense breasts. We simulated the right mammogram for normal controls and the cancer side for MO cancer cases. We created two RCDT images, one from a real mammogram pair and another from a real-simulated mammogram pair. We finetuned a VGG16 on resulting RCDT images to classify the women with MO cancer. We compared the classification performance of the CNN trained on fused RCDT images, CNN
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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.