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Breast cancer is the most common cancers and early detection from mammographyscreening is crucial in improving patient outcomes. Assessing mammographicbreast density is clinically important as the denser breasts have higher riskand are more likely to occlude tumors. Manual assessment by experts is bothtime-consuming and subject to inter-rater variability. As such, there has beenincreased interest in the development of deep learning methods for mammographicbreast density assessment. Despite deep learning having demonstrated impressiveperformance in several prediction tasks for applications in mammography,clinical deployment of deep learning systems in still relatively rare;historically, mammography Computer-Aided Diagnoses (CAD) have over-promised andfailed to deliver. This is in part due to the inability to intuitively quantifyuncertainty of the algorithm for the clinician, which would greatly enhanceusability. Conformal prediction is well suited to increase reliably and trustin deep learning tools but they lack realistic evaluations on medical datasets.In this paper, we present a detailed analysis of three possible applications ofconformal prediction applied to medical imaging tasks: distribution shiftcharacterization, prediction quality improvement, and subgroup fairnessanalysis. Our results show the potential of distribution-free uncertaintyquantification techniques to enhance trust on AI algorithms and expedite theirtranslation to usage.

Three Applications of Conformal Prediction for Rating Breast Density in Mammography