Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms - researchr publication related

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Juhun Lee, Robert M. Nishikawa. Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms. IEEE Trans. Med. Imaging, 41(1):225-236, 2022. [doi]

The following publications are possibly variants of this publication:

Simulating breast mammogram using conditional generative adversarial network: application towards finding mammographically-occult cancerJuhun Lee, Robert M. Nishikawa. micad 2020: [doi]

Detecting mammographically-occult cancer in women with dense breasts using Radon Cumulative Distribution Transform: a preliminary analysisJuhun Lee, Robert M. Nishikawa, Gustavo K. Rohde. micad 2018: 1057508 [doi]

Detecting mammographically-occult cancer in women with dense breasts using deep convolutional neural network and Radon cumulative distribution transformJuhun Lee, Robert M. Nishikawa. micad 2019: 1095003 [doi]

Analyzing GAN artifacts for simulating mammograms: application towards finding mammographically-occult cancerJuhun Lee, Robert M. Nishikawa. micad 2022: [doi]

Developing imaging biomarkers for mammographically-occult cancer in dense breasts using a radiologist's progress rating on cancer development: a preliminary analysisJuhun Lee, Sung Eun Song, Robert M. Nishikawa. iwbi 2018: [doi]

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