Machine Unlearning for Medical Imaging
This addresses the 'right to be forgotten' for patients in medical imaging, but the work is incremental as it highlights limitations of existing methods.
The study evaluated machine unlearning algorithms in medical imaging to remove specific training samples, finding they performed well on retain and forget sets without gender bias but harmed model generalization, especially with larger forget sets, and introduced biases and computational overhead.
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.