Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies

arXiv:2404.08853v1 Announce Type: cross
Abstract: Uncertainty quantification plays a vital role in facilitating the practical implementation of AI in radiology by addressing growing concerns around trustworthiness. Given the challenges associated with acquiring large, annotated datasets in this field, there is a need for methods that enable uncertainty quantification in small data AI approaches tailored to radiology images. In this study, we focused on uncertainty quantification within the context of the small data evolutionary strategies-based technique of deep neuroevolution (DNE). Specifically, …

abstract arxiv challenges concerns cs.cv cs.lg cs.ne datasets evolutionary strategies implementation mri practical quantification radiology role strategies type uncertainty via vital