TY - GEN
T1 - A non-parametric approach to detect epileptogenic lesions using restricted Boltzmann machines
AU - Zhao, Yijun
AU - Ahmed, Bilal
AU - Thesen, Thomas
AU - Blackmon, Karen E.
AU - Dy, Jennifer G.
AU - Brodley, Carla E.
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/8/13
Y1 - 2016/8/13
N2 - Visual detection of lesional areas on a cortical surface is critical in rendering a successful surgical operation for Treatment Resistant Epilepsy (TRE) patients. Unfortunately, 45% of Focal Cortical Dysplasia (FCD, the most common kind of TRE) patients have no visual abnormalities in their brains' 3D-MRI images. We collaborate with doctors from NYU Langone's Comprehensive Epilepsy Center and apply machine learning methodologies to identify the resective zones for these MRI-negative FCD patients. Our task is particularly challenging because MRI images can only provide a limited number of features. Furthermore, data from different patients often exhibit inter-patient variabilities due to age, gender, left/right handedness, etc. In this paper, we introduce a new approach which combines the restricted Boltzmann machines and a Bayesian non-parametric mixture model to address these issues. We demonstrate the efficacy of our model by applying it to a retrospective dataset of MRI-negative FCD patients who are seizure free after surgery.
AB - Visual detection of lesional areas on a cortical surface is critical in rendering a successful surgical operation for Treatment Resistant Epilepsy (TRE) patients. Unfortunately, 45% of Focal Cortical Dysplasia (FCD, the most common kind of TRE) patients have no visual abnormalities in their brains' 3D-MRI images. We collaborate with doctors from NYU Langone's Comprehensive Epilepsy Center and apply machine learning methodologies to identify the resective zones for these MRI-negative FCD patients. Our task is particularly challenging because MRI images can only provide a limited number of features. Furthermore, data from different patients often exhibit inter-patient variabilities due to age, gender, left/right handedness, etc. In this paper, we introduce a new approach which combines the restricted Boltzmann machines and a Bayesian non-parametric mixture model to address these issues. We demonstrate the efficacy of our model by applying it to a retrospective dataset of MRI-negative FCD patients who are seizure free after surgery.
KW - Bayesian non-parametric
KW - Mixture models
KW - Predictive medicine
KW - Restricted Boltzmann machine
KW - Semi-supervised learning and application
UR - http://www.scopus.com/inward/record.url?scp=84984982031&partnerID=8YFLogxK
U2 - 10.1145/2939672.2939705
DO - 10.1145/2939672.2939705
M3 - Conference contribution
AN - SCOPUS:84984982031
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 373
EP - 382
BT - KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
Y2 - 13 August 2016 through 17 August 2016
ER -