DOMINO: Domain-aware loss for deep learning calibration

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. •Deep learning calibration contributes to model trustworthiness and reliability.•DOMINO improves deep learning calibration via a novel domain-aware loss function.•DOMINO designs loss function based on inter-class similarity and class hierarchy.•It is applicable to multiple tasks such as classification, segmentation, and prediction.•This loss can be easily implemented in any existing PyTorch deep learning pipeline.