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

• URL: http://arxiv.org/abs/2410.12402v1
• Date: Wed, 16 Oct 2024 09:31:24 GMT
• Title: De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
• Authors: Moritz Rempe, Lukas Heine, Constantin Seibold, Fabian Hörst, Jens Kleesiek,
• Abstract summary: Open-source tool can be used to de-identify DICOM magnetic resonance images, computer images, whole slide images and magnetic resonance twix raw data. Proposal comprises an elaborate anonymization pipeline for multiple types of inputs, reducing the need for additional tools used for de-identification of imaging data.
• Score: 4.376648893167674
• License:
• Abstract: 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.

Related papers

• VISION: Toward a Standardized Process for Radiology Image Management at the National Level [3.793492459789475]
  We describe our experiences in establishing a trusted collection of radiology images linked to the United States Department of Veterans Affairs (VA) electronic health record database. Key insights include uncovering the specific procedures required for transferring images from a clinical to a research-ready environment.
  arXiv  Detail & Related papers  (2024-04-29T16:30:24Z)
• Medical Image Data Provenance for Medical Cyber-Physical System [8.554664822046966]
  This study proposes using watermarking techniques to embed a device fingerprint (DFP) into captured images. The DFP, representing the unique attributes of the capturing device and raw image, is embedded into raw images before storage. A robust remote validation method is introduced to authenticate images, enhancing the integrity of medical image data in interconnected healthcare systems.
  arXiv  Detail & Related papers  (2024-03-22T13:24:44Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education [1.8078387709049526]
  Transferring medical data "as open as possible" poses a risk to patient privacy. Existing regulations push towards keeping medical data "as closed as necessary" to avoid re-identification risks. This paper explores the legal regulations and terminologies for medical data-sharing.
  arXiv  Detail & Related papers  (2023-07-18T16:53:07Z)
• DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4 [80.36535668574804]
  We develop a novel GPT4-enabled de-identification framework (DeID-GPT") Our developed DeID-GPT showed the highest accuracy and remarkable reliability in masking private information from the unstructured medical text. This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text data processing and de-identification.
  arXiv  Detail & Related papers  (2023-03-20T11:34:37Z)
• ConfounderGAN: Protecting Image Data Privacy with Causal Confounder [85.6757153033139]
  We propose ConfounderGAN, a generative adversarial network (GAN) that can make personal image data unlearnable to protect the data privacy of its owners. Experiments are conducted in six image classification datasets, consisting of three natural object datasets and three medical datasets.
  arXiv  Detail & Related papers  (2022-12-04T08:49:14Z)
• Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks [48.732863591145964]
  We propose a multi-modal convolutional neural network architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels.
  arXiv  Detail & Related papers  (2021-10-12T21:22:24Z)
• Application of Homomorphic Encryption in Medical Imaging [60.51436886110803]
  We show how HE can be used to make predictions over medical images while preventing unauthorized secondary use of data. We report some experiments using 3D chest CT-Scans for a nodule detection task.
  arXiv  Detail & Related papers  (2021-10-12T19:57:12Z)
• Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT [3.8144111843457327]
  Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. In practice, multiple factors such as network issues, accelerated acquisitions, motion artifacts, and imaging protocol design can impede the interpretation of image collections. Here, we create and illustrate a pipeline specifically designed to identify and resolve issues encountered with large-scale data mining of clinically acquired CT data.
  arXiv  Detail & Related papers  (2021-07-27T14:19:08Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things [11.684981995633304]
  Internet of Medical Things (IoMT) can connect many medical imaging equipments to the medical information network. DeepEDN is proposed to fulfill the process of encrypting and decrypting the medical image. The proposed method can achieve a high level of security with a good performance in efficiency.
  arXiv  Detail & Related papers  (2020-04-12T01:42:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.