Prevention of medical errors

LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency

The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) remarkably efficient multimedia information transmissions. The concept of GO-COMS leverages advanced artificial intelligence (AI) tools to address the rising demand for bandwidth efficiency in applications, such as edge computing and Internet-of-Things (IoT). Unlike traditional comm...

SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning

Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner across different locations. Split-Federated (SplitFed) learning is a more recent approach that combines the strengths of FL and SL. SplitFed minimizes the computational burden of FL by balancing computation across cli...

Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network

In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) giv...

Transversal PACS Browser API: Addressing Interoperability Challenges in Medical Imaging Systems

Advances in imaging technologies have revolutionised the medical imaging and healthcare sectors, leading to the widespread adoption of PACS for the ...

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition

Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while...

Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning

This paper proposes a novel federated algorithm that leverages momentum-based variance reduction with adaptive learning to address non-convex settin...

Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track

One of the key goals of artificial intelligence (AI) is the development of a multimodal system that facilitates communication with the visual world ...

Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication

This paper investigates the adversarial robustness of Deep Neural Networks (DNNs) using Information Bottleneck (IB) objectives for task-oriented com...

Virtual Reflections on a Dynamic 2D Eye Model Improve Spatial Reference Identification

The visible orientation of human eyes creates some transparency about people's spatial attention and other mental states. This leads to a dual role ...

A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications

Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcar...

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

The proliferation of Internet of Things (IoT) has increased interest in federated learning (FL) for privacy-preserving distributed data utilization....

Pilot-guided Multimodal Semantic Communication for Audio-Visual Event Localization

Multimodal semantic communication, which integrates various data modalities such as text, images, and audio, significantly enhances communication ef...

Vision Transformer-based Semantic Communications With Importance-Aware Quantization

Semantic communications provide significant performance gains over traditional communications by transmitting task-relevant semantic features throug...

Deep Reinforcement Learning-Based Resource Allocation for Hybrid Bit and Generative Semantic Communications in Space-Air-Ground Integrated Networks

In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image tra...

One Communication Round is All It Needs for Federated Fine-Tuning Foundation Models

The recent advancement of large foundation models (FMs) has increased the demand for fine-tuning these models on large-scale and cross-domain datase...

Adaptive Personalized Over-the-Air Federated Learning with Reflecting Intelligent Surfaces

Over-the-air federated learning (OTA-FL) unifies communication and model aggregation by leveraging the inherent superposition property of the wirele...

An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques

This paper introduces an innovative approach to Attention-deficit/hyperactivity disorder (ADHD) diagnosis by employing deep learning (DL) techniques...

A Miniature Batteryless Bioelectronic Implant Using One Magnetoelectric Transducer for Wireless Powering and PWM Backscatter Communication

Wireless minimally invasive bioelectronic implants enable a wide range of applications in healthcare, medicine, and scientific research. Magnetoelec...

Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder of the central nervous system, primarily manifesting in childhood. It is charac...

Efficient Semantic Communication Through Transformer-Aided Compression

Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature ...