Re-identification from histopathology images

TL;DR

This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy and formulated a risk assessment scheme to estimate the risk to the patient's privacy prior to publication.

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.

Figures (7)

• Figure 1: Overview of randomly selected patches from the three datasets used. In contrast to our in-house meningioma dataset (MEN), the lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC) datasets originating from TCIA exhibit a more pronounced visual variance. Each patch covers an area of about 0.012 square millimeters.
• Figure 2: Scheme of the tissue preparation procedure used to prepare the slides in the in-house meningioma (MEN) dataset. A resection can be divided into one or more containers, each of which can be further divided into one or more blocks. However, only one slide from each block is included in the data set.
• Figure 3: Scheme of how the online stain augmentation was applied during MIL training. During training, each of the images within one bag was augmented separately.
• Figure 4: Given are versions of the same patch to which different intensities of stain augmentation were applied. A stain augmentation based on the Macenkos stain normalization method was used. The non augmented patch is given in the center of the grid.
• Figure 5: Overview of the experimental setup of Experiments 1 and 2. Experiment 1 involved a tenfold Monte Carlo cross-validation. In Experiment 2, the slides from the earliest resection were used for training, while all images from later resections were used in a hold-out test dataset. To increase the statistical validity of the results of Experiment 2, ten models for each algorithm were trained on ten randomly selected training and validation splits drawn from the earliest resection of each patient.