A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data
Daniel Schaudt,
Christian Späte,
Reinhold von Schwerin
et al.
Abstract:In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial quantities of high-quality data, a resource often lacking in numerous medical imaging scenarios. One way to overcome this deficiency is to artificially generate such images. Therefore, in this comparative study we train five generative models to artificially inc… Show more
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