DOMINO++: Domain-aware Loss Regularization for Deep Learning
  Generalizability
Skylar E. Stolte Kyle Volle Aprinda Indahlastari Alejandro Albizu Adam J. Woods Kevin Brink Matthew Hale Ruogu Fang
Out-of-distribution (OOD) generalization poses a serious challenge for modern
deep learning (DL). OOD data consists of test data that is significantly
different from the model's training data. DL models that perform well on
in-domain test data could struggle on OOD data. Overcoming this discrepancy is
essential to the reliable deployment of DL. Proper model calibration decreases
the number of spurious connections that are made between model features and
class outputs. Hence, calibrated DL can improve OOD generalization by only
learning features that are truly indicative of the respective classes. Previous
work proposed domain-aware model calibration (DOMINO) to improve DL
calibration, but it lacks designs for model generalizability to OOD data. In
this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss
regularization focused on OOD generalizability. DOMINO++ integrates
expert-guided and data-guided knowledge in its regularization. Unlike DOMINO
which imposed a fixed scaling and regularization rate, DOMINO++ designs a
dynamic scaling factor and an adaptive regularization rate. Comprehensive
evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue
segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data
consists of synthetic noisy and rotated datasets, as well as real data using a
different MRI scanner from a separate site. DOMINO++'s superior performance
demonstrates its potential to improve the trustworthy deployment of DL on real
clinical data.