  To measure the volume of specific image structures, a typical approach is to
first segment those structures using a neural network trained on voxel-wise
(strong) labels and subsequently compute the volume from the segmentation. A
more straightforward approach would be to predict the volume directly using a
neural network based regression approach, trained on image-level (weak) labels
indicating volume.
  In this article, we compared networks optimized with weak and strong labels,
and study their ability to generalize to other datasets. We experimented with
white matter hyperintensity (WMH) volume prediction in brain MRI scans. Neural
networks were trained on a large local dataset and their performance was
evaluated on four independent public datasets. We showed that networks
optimized using only weak labels reflecting WMH volume generalized better for
WMH volume prediction than networks optimized with voxel-wise segmentations of
WMH. The attention maps of networks trained with weak labels did not seem to
delineate WMHs, but highlighted instead areas with smooth contours around or
near WMHs. By correcting for possible confounders we showed that networks
trained on weak labels may have learnt other meaningful features that are more
suited to generalization to unseen data. Our results suggest that for imaging
biomarkers that can be derived from segmentations, training networks to predict
the biomarker directly may provide more robust results than solving an
intermediate segmentation step.
