Abstract
Rights provisioned within data protection regulations, permit patients to request that knowledge about their information be eliminated by data holders. With the advent of AI learned on data, one can imagine that such rights can extent to requests for forgetting knowledge of patient’s data within AI models. However, forgetting patients’ imaging data from AI models, is still an under-explored problem. In this paper, we study the influence of patient data on model performance and formulate two hypotheses for a patient’s data: either they are common and similar to other patients or form edge cases, i.e. unique and rare cases. We show that it is not possible to easily forget patient data. We propose a targeted forgetting approach to perform patient-wise forgetting. Extensive experiments on the benchmark Automated Cardiac Diagnosis Challenge dataset showcase the improved performance of the proposed targeted forgetting approach as opposed to a state-of-the-art method.
R. Su and X. Liu—Contributed equally.
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Notes
- 1.
There is also a connection between edge cases and active learning [20], where one aims to actively label diverse data to bring more information to the model.
- 2.
For our experiments we fix to introduce noise to 1\(\%\) most informative weights (based on extensive experiments) when applying the targeted forgetting.
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Acknowledgements
This work was supported by the University of Edinburgh, the Royal Academy of Engineering and Canon Medical Research Europe by a PhD studentship to Xiao Liu. This work was partially supported by the Alan Turing Institute under EPSRC grant EP/N510129/1. S.A. Tsaftaris acknowledges the support of Canon Medical and the Royal Academy of Engineering and the Research Chairs and Senior Research Fellowships scheme (grant RC-SRF1819\(\backslash \)8\(\backslash \)25) and the [in part] support of the Industrial Centre for AI Research in digitalDiagnostics (iCAIRD, https://icaird.com) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690].
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Su, R., Liu, X., Tsaftaris, S.A. (2022). Why Patient Data Cannot Be Easily Forgotten?. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_60
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