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30 March 2024 Effect of the prior distribution on a Bayesian model or errors of type for transcranial magnetic stimulation
John S. H. Baxter, Pierre Jannin
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Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129280P (2024) https://doi.org/10.1117/12.3008324
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Measuring errors in neuro-interventional pointing tasks is critical to better evaluating human experts as well as machine learning algorithms. If the target may be highly ambiguous, different experts may fundamentally select different targets, believing them to refer to the same region, a phenomenon called an error of type. This paper investigates the effects of changing the prior distribution on a Bayesian model for errors of type specific to transcranial magnetic stimulation (TMS) planning. Our results show that a particular prior can be chosen which is analytically solvable, removes spurious modes, and returns estimates that are coherent with the TMS literature. This is a step towards a fully rigorous model that can be used in system evaluation and machine learning.
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John S. H. Baxter and Pierre Jannin "Effect of the prior distribution on a Bayesian model or errors of type for transcranial magnetic stimulation", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129280P (30 March 2024); https://doi.org/10.1117/12.3008324
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KEYWORDS
Magnetism
Data modeling
Machine learning
Analytics
Computation time
Computer simulations
Detection and tracking algorithms
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