Skip to yearly menu bar Skip to main content

Workshop: Medical Imaging Meets NeurIPS

Clinical Validation of Machine Learning Algorithm Generated Images

Young Joon Kwon


Generative machine learning (ML) methods can reduce time, cost, and radiation associated with medical image acquisition, compression, or generation techniques. While quantitative metrics are commonly used in the evaluation of ML generated images, it is unknown how well these quantitative metrics relate to the diagnostic utility of images. Here, fellowship-trained radiologists provided diagnoses and qualitative evaluations on chest radiographs reconstructed from the current standard JPEG2000 or variational autoencoder (VAE) techniques. Cohen’s kappa coefficient measured the agreement of diagnoses based on different reconstructions. Methods that produced similar Fréchet inception distance (FID) showed similar diagnostic performances. Thus in place of time-intensive expert radiologist verification, an appropriate target FID -- an objective quantitative metric -- can evaluate the clinical utility of ML generated medical images.

Chat is not available.