  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 address the problem of simultaneously learning multiple
classifiers in the MTL framework when the training data has imprecise labels.
We assume that there is an additional source of information that provides a
score for each instance which reflects the certainty about its label. Modeling
this score as being generated by an underlying ranking function, we augment the
MTL framework with an added layer of supervision. This results in new MTL
methods that are able to learn accurate classifiers while preserving the domain
structure provided through the rank information. We apply these methods to the
task of detecting abnormal cortical regions in the MRIs of patients suffering
from 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 additional
source of label information. Our proposed methods are able to successfully
detect abnormal regions for all patients in our dataset and achieve a higher
performance as compared to baseline methods.
