Analyzing GAN artifacts for simulating mammograms: application towards finding mammographically-occult cancer
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SPIE
Reference7 articles.
1. Detecting mammographically occult cancer in women with dense breasts using deep convolutional neural network and Radon Cumulative Distribution Transform
2. Image-to-Image Translation with Conditional Adversarial Networks
3. Simulating breast mammogram using Conditional Generative Adversarial Network: application towards finding mammographically-occult cancer
4. Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Developing classification and segmentation algorithms for GAN-generated mammographic artifacts based on radiologist annotation;Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications;2024-04-02
2. Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer;Journal of Medical Imaging;2023-10-12
3. Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms;Signals;2023-06-01
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