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Does Generative Adversarial Network Ensure Diversity in Data? Morphologic Evaluation of Synthetic Isocitrate Dehydrogenase-Mutant Glioblastomas in A Clinical Diagnostic Model

2020-12-28

Abstract excerpt

Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures diversity is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variability to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant hig...

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Literature Corpus work: 3635f66b-64cd-5d97-89b3-84b1bb9d11cc
DOI: 10.21203/rs.3.rs-126408/v1

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