We successfully utilized OCLF ORNL GPU computing resources for efficient uncertainty analysis, which addressed the computational overhead caused by our proposed probabilistic models.
We propose a novel deep learning method that achieves 170X average speed up compared to the original probabilistic marching cubes algorithm [1] implementation and performs predictions with an accuracy comparable to the original algorithm.
We propose the application of various visualization techniques, such as probability maps, confidence maps, level sets, and topology-based visualizations, to effectively communicate the uncertainty in source localization with clinicians.
