An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition

arXiv:2309.00903v2 Announce Type: replace
Abstract: Explainable AI is crucial in medical imaging. In the challenging field of neuroscience, visual topics present a high level of complexity, particularly within three-dimensional space. The application of neuroscience, which involves identifying brain sulcal features from MRI, faces significant hurdles due to varying annotation protocols among experts and the intricate three-dimension functionality of the brain. Consequently, traditional explainability approaches fall short in effectively validating and evaluating these networks. To address this, we first present a …

abstract application arxiv brain complexity cs.ai cs.cv explainable ai features framework imaging look medical medical imaging mri neuroscience patterns recognition space three-dimensional topics type visual