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Multi-task learning (MTL) is useful for domains in which data originates frommultiple sources that are individually under-sampled. MTL methods are able tolearn classification models that have higher performance as compared tolearning a single model by aggregating all the data together or learning aseparate model for each data source. The performance of these methods relies onlabel accuracy. We address the problem of simultaneously learning multipleclassifiers in the MTL framework when the training data has imprecise labels.We assume that there is an additional source of information that provides ascore for each instance which reflects the certainty about its label. Modelingthis score as being generated by an underlying ranking function, we augment theMTL framework with an added layer of supervision. This results in new MTLmethods that are able to learn accurate classifiers while preserving the domainstructure provided through the rank information. We apply these methods to thetask of detecting abnormal cortical regions in the MRIs of patients sufferingfrom focal epilepsy whose MRI were read as normal by expert neuroradiologists.In addition to the noisy labels provided by the results of surgical resection,we employ the results of an invasive intracranial-EEG exam as an additionalsource of label information. Our proposed methods are able to successfullydetect abnormal regions for all patients in our dataset and achieve a higherperformance as compared to baseline methods.

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