# Re-identification from histopathology images Source: [Re-identification from histopathology images]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:36:51.485Z Authors: Jonathan Ganz, Jonas Ammeling, Samir Jabari, Katharina Breininger, Marc Aubreville Published: 2024-03-19T15:15:19.000Z ## Abstract In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm's performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with F1 scores of 50.16 % and 52.30 % on the LSCC and LUAD datasets, respectively, and with 62.31 % on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient's privacy prior to publication. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Medical Image Re-identification on LUAD Histopathology](): Medical Image Re-identification; LUAD Histopathology; CMC-R1: 56.76. - [Medical Image Re-identification on LIHC Abdominal-CT](): Medical Image Re-identification; LIHC Abdominal-CT; CMC-R1: 33.93. - [Medical Image Re-identification on KIRC Abdominal-CT](): Medical Image Re-identification; KIRC Abdominal-CT; CMC-R1: 39.29. - [Medical Image Re-identification on ODIR Fundus](): Medical Image Re-identification; ODIR Fundus; CMC-R1: 22.65. - [Medical Image Re-identification on OASIS Brain-MRI 2](): Medical Image Re-identification; OASIS Brain-MRI 2; CMC-R1: 28. - [Medical Image Re-identification on GRAPE Fundus](): Medical Image Re-identification; GRAPE Fundus; CMC-R1: 25.08. - [Medical Image Re-identification on MIMIC-CXR](): Medical Image Re-identification; MIMIC-CXR; CMC-R1: 11.4. - [Medical Re-identification on ChestX-Ray14](): Medical Image Re-identification; ChestX-ray14; Rank-1 Accuracy: 11.9. - [Medical Image Re-identification on HCC-TACE Abdominal-CT](): Medical Image Re-identification; HCC-TACE Abdominal-CT; CMC-R1: 33.33. - [Medical Image Re-identification on Mess2 Fundus](): Medical Image Re-identification; Mess2 Fundus; CMC-R1: 27.43. - [Medical Image Re-identification on CCII Lung-CT](): Medical Image Re-identification; CCII Lung-CT; CMC-R1: 53.62. ## Original sources and implementation [Original paper]() [PDF]()