  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.
