AI Medical Compendium Topic

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Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups.

Sensors (Basel, Switzerland)
Deep learning pervades heavy data-driven disciplines in research and development. The Internet of Things and sensor systems, which enable smart environments and services, are settings where deep learning can provide invaluable utility. However, the d...

Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns.

The Journal of arthroplasty
BACKGROUND: In this work, we applied and validated an artificial intelligence technique known as generative adversarial networks (GANs) to create large volumes of high-fidelity synthetic anteroposterior (AP) pelvis radiographs that can enable deep le...

Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model.

IEEE journal of biomedical and health informatics
Theabnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision o...

In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review.

Sensors (Basel, Switzerland)
The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare sys...

DINI: data imputation using neural inversion for edge applications.

Scientific reports
The edge computing paradigm has recently drawn significant attention from industry and academia. Due to the advantages in quality-of-service metrics, namely, latency, bandwidth, energy efficiency, privacy, and security, deploying artificial intellige...

Customized Federated Learning for Multi-Source Decentralized Medical Image Classification.

IEEE journal of biomedical and health informatics
The performance of deep networks for medical image analysis is often constrained by limited medical data, which is privacy-sensitive. Federated learning (FL) alleviates the constraint by allowing different institutions to collaboratively train a fede...

PrivacyMask: Real-world privacy protection in face ID systems.

Mathematical biosciences and engineering : MBE
Recent works have illustrated that many facial privacy protection methods are effective in specific face recognition algorithms. However, the COVID-19 pandemic has promoted the rapid innovation of face recognition algorithms for face occlusion, espec...

Privacy Preserving Defense For Black Box Classifiers Against On-Line Adversarial Attacks.

IEEE transactions on pattern analysis and machine intelligence
Deep learning models have been shown to be vulnerable to adversarial attacks. Adversarial attacks are imperceptible perturbations added to an image such that the deep learning model misclassifies the image with a high confidence. Existing adversarial...

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.

Journal of medical Internet research
BACKGROUND: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different a...

MixNN: A Design for Protecting Deep Learning Models.

Sensors (Basel, Switzerland)
In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters since the model consists of several layers and each layer contains its own structure and parameters. The layers in a deep learning mod...