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Deep learning has achieved the state-of-the-art performance across medicalimaging tasks; however, model calibration is often not considered. Uncalibratedmodels are potentially dangerous in high-risk applications since the user doesnot know when they will fail. Therefore, this paper proposes a noveldomain-aware loss function to calibrate deep learning models. The proposed lossfunction applies a class-wise penalty based on the similarity between classeswithin a given target domain. Thus, the approach improves the calibration whilealso ensuring that the model makes less risky errors even when incorrect. Thecode for this software is available at https://github.com/lab-smile/DOMINO.

DOMINO: Domain-aware Loss for Deep Learning Calibration