  Trustworthy deployment of deep learning medical imaging models into
real-world clinical practice requires that they be calibrated. However, models
that are well calibrated overall can still be poorly calibrated for a
sub-population, potentially resulting in a clinician unwittingly making poor
decisions for this group based on the recommendations of the model. Although
methods have been shown to successfully mitigate biases across subgroups in
terms of model accuracy, this work focuses on the open problem of mitigating
calibration biases in the context of medical image analysis. Our method does
not require subgroup attributes during training, permitting the flexibility to
mitigate biases for different choices of sensitive attributes without
re-training. To this end, we propose a novel two-stage method: Cluster-Focal to
first identify poorly calibrated samples, cluster them into groups, and then
introduce group-wise focal loss to improve calibration bias. We evaluate our
method on skin lesion classification with the public HAM10000 dataset, and on
predicting future lesional activity for multiple sclerosis (MS) patients. In
addition to considering traditional sensitive attributes (e.g. age, sex) with
demographic subgroups, we also consider biases among groups with different
image-derived attributes, such as lesion load, which are required in medical
image analysis. Our results demonstrate that our method effectively controls
calibration error in the worst-performing subgroups while preserving prediction
performance, and outperforming recent baselines.
