Medical Image Data Provenance for Medical Cyber-Physical System
- URL: http://arxiv.org/abs/2403.15522v1
- Date: Fri, 22 Mar 2024 13:24:44 GMT
- Title: Medical Image Data Provenance for Medical Cyber-Physical System
- Authors: Vijay Kumar, Kolin Paul,
- Abstract summary: 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.
- Score: 8.554664822046966
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of fraudulent practices by healthcare professionals and social workers exchanging unverified images via mobile applications. To mitigate these risks, this study proposes using watermarking techniques to embed a device fingerprint (DFP) into captured images, ensuring data provenance. The DFP, representing the unique attributes of the capturing device and raw image, is embedded into raw images before storage, thus enabling verification of image authenticity and source. Moreover, a robust remote validation method is introduced to authenticate images, enhancing the integrity of medical image data in interconnected healthcare systems. Through a case study on mobile fundus imaging, the effectiveness of the proposed framework is evaluated in terms of computational efficiency, image quality, security, and trustworthiness. This approach is suitable for a range of applications, including telemedicine, the Internet of Medical Things (IoMT), eHealth, and Medical Cyber-Physical Systems (MCPS) applications, providing a reliable means to maintain data provenance in diagnostic settings utilizing medical images or videos.
Related papers
- Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models.
Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
arXiv Detail & Related papers (2024-10-23T00:31:17Z) - De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy [4.376648893167674]
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.
arXiv Detail & Related papers (2024-10-16T09:31:24Z) - FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection [83.54960238236548]
FEDMEKI not only preserves data privacy but also enhances the capability of medical foundation models.
FEDMEKI allows medical foundation models to learn from a broader spectrum of medical knowledge without direct data exposure.
arXiv Detail & Related papers (2024-08-17T15:18:56Z) - Plaintext-Free Deep Learning for Privacy-Preserving Medical Image Analysis via Frequency Information Embedding [9.192156293063414]
This paper proposes a novel framework that uses surrogate images for analysis.
The framework is called Frequency-domain Exchange Style Fusion (FESF)
Our framework effectively preserves the privacy of medical images and maintains diagnostic accuracy of DL models at a relatively high level, proving its effectiveness across various datasets and DL-based models.
arXiv Detail & Related papers (2024-03-25T06:56:38Z) - Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems [1.8434042562191815]
The Internet of Medical Things (IoMT) transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention.
Its benefits are countered by significant security challenges that endanger the lives of its users due to the sensitivity and value of the processed data.
A new framework for Intrusion Detection Systems (IDS) is introduced, leveraging Artificial Neural Networks (ANN) for intrusion detection while utilizing Federated Learning (FL) for privacy preservation.
arXiv Detail & Related papers (2024-03-14T11:57:26Z) - Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial
Watermarking [43.17275405041853]
We present a pioneering solution named Medical Image Adversarial watermarking (MIAD-MARK)
Our approach introduces watermarks that strategically mislead unauthorized AI diagnostic models, inducing erroneous predictions without compromising the integrity of the visual content.
Our solution effectively mitigates unauthorized exploitation of medical images even in the presence of sophisticated watermark removal networks.
arXiv Detail & Related papers (2023-03-17T09:37:41Z) - COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical
Network to Monitor and Detect COVID-19 Infection from Point-of-Care
Ultrasound Images [66.63200823918429]
COVID-Net USPro monitors and detects COVID-19 positive cases with high precision and recall from minimal ultrasound images.
The network achieves 99.65% overall accuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when trained with only 5 shots.
arXiv Detail & Related papers (2023-01-04T16:05:51Z) - A Trustworthy Framework for Medical Image Analysis with Deep Learning [71.48204494889505]
TRUDLMIA is a trustworthy deep learning framework for medical image analysis.
It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
arXiv Detail & Related papers (2022-12-06T05:30:22Z) - An Interpretable Multiple-Instance Approach for the Detection of
referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images.
By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy.
We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z) - Privacy-preserving medical image analysis [53.4844489668116]
We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging.
We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets.
We empirically evaluate the framework's security against a gradient-based model inversion attack.
arXiv Detail & Related papers (2020-12-10T13:56:00Z)
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.