1 citations · 1 across the 3 of their papers we have counts for
3 papers
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
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 tr…
DOMINO: Domain-aware Loss for Deep Learning Calibration
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially da…
DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applicatio…