medical-2
Radiologist Binary Classification
3260 papers • 126 benchmarks • 313 datasets
This task measures a radiologist's performance on distinguishing between generated (e.g. with a GAN, VAE, etc.) and real images, ascribing to the high visual quality of the synthesized images, and to their potential use in advancing and facilitating downstream medical tasks.
(Image credit: Papersgraph)
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Most implemented papers
MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education
Cyril Zakka, Ghida Saheb, Elie Najem, G. Berjawi•Sat Oct 10 2020
A generative adversarial network is trained to synthesize 512 x 512 high-resolution mammograms to distinguish between generated and real images, ascribing to the high visual quality of the synthesized and edited mammograms, and to their potential use in advancing and facilitating medical education.
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