AIMC Topic: Privacy

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Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.

Medical physics Feb 9, 2024
BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitat...
Male Middle Aged Humans Female Prognosis COVID-19 Aged Tomography, X-Ray Computed Datasets as Topic Deep Learning Radiography, Thoracic Privacy
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Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet.

Network (Bristol, England) Jan 31, 2024
This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based l...
Computer Security Cloud Computing Privacy Biometric Identification Blockchain Humans Deep Learning
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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study.

Journal of medical Internet research Jan 25, 2024
BACKGROUND: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unst...
Artificial Intelligence Electronic Health Records Humans Natural Language Processing Privacy China
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Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 11, 2024
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in par...
Deep Learning Privacy Electronic Health Records Humans Medical Informatics
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Secret learning for lung cancer diagnosis-a study with homomorphic encryption, texture analysis and deep learning.

Biomedical physics & engineering express Dec 8, 2023
Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs...
Computer Security Privacy Humans Lung Deep Learning Lung Neoplasms
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Dynamic Corrected Split Federated Learning With Homomorphic Encryption for U-Shaped Medical Image Networks.

IEEE journal of biomedical and health informatics Dec 5, 2023
U-shaped networks have become prevalent in various medical image tasks such as segmentation, and restoration. However, most existing U-shaped networks rely on centralized learning which raises privacy concerns. To address these issues, federated lear...
Diagnostic Imaging Privacy Machine Learning
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Neural networks memorise personal information from one sample.

Scientific reports Dec 4, 2023
Deep neural networks (DNNs) have achieved high accuracy in diagnosing multiple diseases/conditions at a large scale. However, a number of concerns have been raised about safeguarding data privacy and algorithmic bias of the neural network models. We ...
Neural Networks, Computer Humans Privacy
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An easy-to-use artificial intelligence preoperative lymph node metastasis predictor (LN-MASTER) in rectal cancer based on a privacy-preserving computing platform: multicenter retrospective cohort study: erratum.

International journal of surgery (London, England) Dec 1, 2023
Rectal Neoplasms Humans Artificial Intelligence Lymphatic Metastasis Retrospective Studies Privacy
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Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

IEEE transactions on neural networks and learning systems Nov 30, 2023
Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes...
Neural Networks, Computer Students Emotions Electroencephalography Humans Privacy
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LFighter: Defending against the label-flipping attack in federated learning.

Neural networks : the official journal of the International Neural Network Society Nov 11, 2023
Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in their devices. However, that same autonomy opens the door for malicious ...
Poisons Reproducibility of Results Neurons Privacy Machine Learning
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