Multi-task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy


Bilal Ahmed, Thomas Thesen, Karen Blackmon, Ruben Kuzniecky, Orrin Devinsky, Jennifer Dy, Carla Brodley ;
Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:115-133, 2016.


Multi-task learning (MTL) is useful for domains in which data originates from multiple sources that are individually under-sampled. MTL methods are able to learn classification models that have higher performance as compared to learning a single model by aggregating all the data together or learning a separate model for each data source. The performance of these methods relies on label accuracy. We develop two models that address the problem of multitask learning when the training data has imprecise labels. We apply these methods to the task of detecting abnormal cortical regions in the MRIs of patients suffering from epilepsy whose MRI were deemed normal by neuroradiologists. We use the results of intracranial-EEG exam as an auxiliary source of supervision. The proposed methods successfully detect abnormal regions for all patients in our sample and achieve higher performance as compared to other methods.

Related Material