ArticleFrontiers in oncology2022
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- The Role of Synthetic Data and Generative AI in Breast Imaging: Promise, Pitfalls, and Pathways Forward.Diagnostics (Basel, Switzerland) · 2025Review
- 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
- Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Simulating mismatch between calibration and target population in AI for mammography the retrospective VAIB study.NPJ digital medicine · 2025Article
- Neural Network-Based Mammography Analysis: Augmentation Techniques for Enhanced Cancer Diagnosis-A Review.Bioengineering (Basel, Switzerland) · 2025Review
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.BMJ (Clinical research ed.) · 2025Article
- Article
- Attention-Based Deep Learning System for Classification of Breast Lesions-Multimodal, Weakly Supervised Approach.Cancers · 2023Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
Abstract
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in high-resolution mammograms. The training images were split by breast density BI-RADS categories, being
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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.