@inproceedings{
 title = {A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma},
 type = {inproceedings},
 year = {2011},
 id = {4df3b801-1f44-3725-ad1c-bdbf0782df94},
 created = {2023-10-25T08:56:40.876Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:40.876Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 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.},
 bibtype = {inproceedings},
 author = {Lai, Y. and Viswanath, S. and Baccon, J. and Ellison, D. and Judkins, A.R. and Madabhushi, A.},
 doi = {10.1109/NEBC.2011.5778641},
 booktitle = {2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011}
}

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