  Machine unlearning is the process of removing the impact of a particular set
of training samples from a pretrained model. It aims to fulfill the "right to
be forgotten", which grants the individuals such as patients the right to
reconsider their contribution in models including medical imaging models. In
this study, we evaluate the effectiveness (performance) and computational
efficiency of different unlearning algorithms in medical imaging domain. Our
evaluations demonstrate that the considered unlearning algorithms perform well
on the retain set (samples whose influence on the model is allowed to be
retained) and forget set (samples whose contribution to the model should be
eliminated), and show no bias against male or female samples. They, however,
adversely impact the generalization of the model, especially for larger forget
set sizes. Moreover, they might be biased against easy or hard samples, and
need additional computational overhead for hyper-parameter tuning. In
conclusion, machine unlearning seems promising for medical imaging, but the
existing unlearning algorithms still needs further improvements to become more
practical for medical applications.
