Radiologist Binary Classification

1 papers with code • 0 benchmarks • 0 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.

Most implemented papers

MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education

cyrilzakka/stylegan2-tpu 11 Oct 2020

During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions.