@misc{Lai2011,
   abstract = {Medulloblastoma (MB) is the most common brain tumor in children. There are four distinct subtypes of MB, but patients with anaplastic/large cell have the worst prognosis. Since the morbidity is highly correlated with treatment for MB, the ability to distinguish aggressive (such as anaplastic/large cell) MB is crucial. We present a scheme that leverages quantitative image texture features (Haar, Haralick, and Laws) and classifier ensembles (random forests) to automatically classify histological images from MB resection as being anaplastic/large cell or non-anaplastic/large cell. Preliminary results for our scheme when applied to patch-based classification of MB specimens yield an AUC of 0.91. © 2011 IEEE.},
   author = {Y. Lai and Satish E. Viswanath and J. Baccon and D.W. Ellison and A.R. Judkins and A. Madabhushi},
   doi = {10.1109/NEBC.2011.5778641},
   isbn = {9781612848273},
   journal = {2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011},
   title = {A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma},
   year = {2011},
}

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