Why patient data cannot be easily forgotten?

Ruolin Su, Xiao Liu, Sotirios A. Tsaftaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022
Subtitle of host publication25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII
Number of pages11
ISBN (Electronic)978-3-031-16452-1
ISBN (Print)978-3-031-16451-4
Publication statusE-pub ahead of print - 16 Sept 2022
Event25th International Conference on Medical Image Computing and Computer Assisted Intervention - Resorts World Sentosa (RWS), Sentosa Island, Singapore
Duration: 18 Sept 202222 Sept 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2022
CitySentosa Island
Internet address

Keywords / Materials (for Non-textual outputs)

  • privacy
  • Patient-wise Forgetting
  • Scrubbing
  • Learning
  • Privacy
  • Patient-wise forgetting


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