Unsupervised bias discovery in medical image segmentation
Nicolás Gaggion Rodrigo Echeveste Lucas Mansilla Diego H. Milone Enzo Ferrante
It has recently been shown that deep learning models for anatomical
segmentation in medical images can exhibit biases against certain
sub-populations defined in terms of protected attributes like sex or ethnicity.
In this context, auditing fairness of deep segmentation models becomes crucial.
However, such audit process generally requires access to ground-truth
segmentation masks for the target population, which may not always be
available, especially when going from development to deployment. Here we
propose a new method to anticipate model biases in biomedical image
segmentation in the absence of ground-truth annotations. Our unsupervised bias
discovery method leverages the reverse classification accuracy framework to
estimate segmentation quality. Through numerical experiments in synthetic and
realistic scenarios we show how our method is able to successfully anticipate
fairness issues in the absence of ground-truth labels, constituting a novel and
valuable tool in this field.