AMIA Annu Symp Proc - An automated pathological class level annotation system for volumetric brain images.

Tópicos

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{ data(3008) multipl(1320) sourc(1022) }
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Resumo

We introduce an automated, pathological class level annotation system for medical volumetric brain images. While much of the earlier work has mainly focused on annotating regions of interest in medical images, our system does not require annotated region level training data nor assumes perfect segmentation results for the regions of interest; the time and effort needed for acquiring training data are hence significantly reduced. This capability of handling high-dimensional noisy data, however, poses additional technical challenges, since statistical estimation of models for such data is prone to over-fitting. We propose a framework that combines a regularized logistic regression method and a kernel-based discriminative method to address these problems. Regularized methods provide a flexible selection mechanism that is well-suited for high dimensional noisy data. Our experiments show promising results in classifying computer tomography images of traumatic brain injury patients into pathological classes.

Resumo Limpo

introduc autom patholog class level annot system medic volumetr brain imag much earlier work main focus annot region interest medic imag system requir annot region level train data assum perfect segment result region interest time effort need acquir train data henc signific reduc capabl handl highdimension noisi data howev pose addit technic challeng sinc statist estim model data prone overfit propos framework combin regular logist regress method kernelbas discrimin method address problem regular method provid flexibl select mechan wellsuit high dimension noisi data experi show promis result classifi comput tomographi imag traumat brain injuri patient patholog class

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