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Deep learning-based auto-segmentation of paraganglioma for growth monitoring
We assess the performance of the model based on visual inspection by a senior otorhinolaryngologist and several quantitative metrics by comparing model outputs with manual delineations, including a comparison with variation in manual delineation by multiple observers.
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals
The aim of this work is to learn the general underlying growth pattern of paragangliomas from multiple tumor growth data sets, in which each data set contains a tumor's volume over time.
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