DOMINO: Domain-aware Loss for Deep Learning Calibration

2023/02/10 by Skylar E. Stolte, Stolte, Skylar E., Kyle Volle +13
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Imaging Techniques and Applications #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2302.05142

openalex publication_date 2023/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even when incorrect. The code for this software is available at https://github.com/lab-smile/DOMINO.

Related

• DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
• DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
• Calibrating Deep Neural Network using Euclidean Distance
• Addressing catastrophic forgetting for medical domain expansion
• Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation