Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer.

Juhun LeeTamerlan MustafaevRobert M Nishikawa

Published in: Journal of medical imaging (Bellingham, Wash.) (2023)

We found that artifacts were pervasive in the CGAN-simulated mammograms. However, they did not negatively affect our MO cancer detection algorithm; the simulated mammograms still provided complementary information for MO cancer detection when combined with real mammograms.

Keyphrases

papillary thyroid
squamous cell
squamous cell carcinoma
childhood cancer
magnetic resonance
deep learning
loop mediated isothermal amplification
neural network
contrast enhanced