  We consider a model-agnostic solution to the problem of Multi-Domain Learning
(MDL) for multi-modal applications. Many existing MDL techniques are
model-dependent solutions which explicitly require nontrivial architectural
changes to construct domain-specific modules. Thus, properly applying these MDL
techniques for new problems with well-established models, e.g. U-Net for
semantic segmentation, may demand various low-level implementation efforts. In
this paper, given emerging multi-modal data (e.g., various structural
neuroimaging modalities), we aim to enable MDL purely algorithmically so that
widely used neural networks can trivially achieve MDL in a model-independent
manner. To this end, we consider a weighted loss function and extend it to an
effective procedure by employing techniques from the recently active area of
learning-to-learn (meta-learning). Specifically, we take inner-loop gradient
steps to dynamically estimate posterior distributions over the hyperparameters
of our loss function. Thus, our method is model-agnostic, requiring no
additional model parameters and no network architecture changes; instead, only
a few efficient algorithmic modifications are needed to improve performance in
MDL. We demonstrate our solution to a fitting problem in medical imaging,
specifically, in the automatic segmentation of white matter hyperintensity
(WMH). We look at two neuroimaging modalities (T1-MR and FLAIR) with
complementary information fitting for our problem.
