  Hand-crafted features extracted from dynamic contrast-enhanced magnetic
resonance images (DCE-MRIs) have shown strong predictive abilities in
characterization of breast lesions. However, heterogeneity across medical image
datasets hinders the generalizability of these features. One of the sources of
the heterogeneity is the variation of MR scanner magnet strength, which has a
strong influence on image quality, leading to variations in the extracted image
features. Thus, statistical decision algorithms need to account for such data
heterogeneity. Despite the variations, we hypothesize that there exist
underlying relationships between the features extracted from the datasets
acquired with different magnet strength MR scanners. We compared the use of a
multi-task learning (MTL) method that incorporates those relationships during
the classifier training to support vector machines run on a merged dataset that
includes cases with various MRI strength images. As a result, higher predictive
power is achieved with the MTL method.
