# De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy

arXiv: https://arxiv.org/abs/2410.12402 · HTML: https://mlsanity.com/paper/2410.12402/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data](https://mlsanity.com/paper/2103.08562/) (2021)
- [Anonymization of Whole Slide Images in Histopathology for Research and Education](https://mlsanity.com/paper/2211.06103/) (2022)

## Built on by (most cited first, 7 of 7)

- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images](https://mlsanity.com/paper/2501.09552/) (2025)
- [From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction](https://mlsanity.com/paper/2604.11376/) (2026)
- [Medical Image De-Identification Benchmark Challenge](https://mlsanity.com/paper/2507.23608/) (2025)
- [Towards Selection of Large Multimodal Models as Engines for Burned-in Protected Health Information Detection in Medical Images](https://mlsanity.com/paper/2511.02014/) (2025)
- [Privacy in Image Datasets: A Case Study on Pregnancy Ultrasounds](https://mlsanity.com/paper/2602.07149/) (2026)
- [Masking Is Not Enough: Generative Restoration for Multimodal De-Identification in Medical AI](https://mlsanity.com/paper/2608.21133/) (2026)

## Abstract (arXiv)

Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Consequently, these types of data must be pseudonymized prior to utilisation, which presents a significant challenge for many researchers. Given the vast array of medical data, it is necessary to employ a variety of de-identification techniques. To facilitate the anonymization process for medical imaging data, we have developed an open-source tool that can be used to de-identify DICOM magnetic resonance images, computer tomography images, whole slide images and magnetic resonance twix raw data. Furthermore, the implementation of a neural network enables the removal of text within the images. The proposed tool automates an elaborate anonymization pipeline for multiple types of inputs, reducing the need for additional tools used for de-identification of imaging data. We make our code publicly available at https://github.com/code-lukas/medical_image_deidentification.