A5036931674- Radiomics and Machine Learning in Medical Imaging
- Advanced Memory and Neural Computing
- COVID-19 diagnosis using AI
- Neural Networks and Reservoir Computing
- AI in cancer detection
- Neural dynamics and brain function
- Hernia repair and management
- Intestinal and Peritoneal Adhesions
- Artificial Intelligence in Healthcare and Education
- Appendicitis Diagnosis and Management
- Radiology practices and education
- Machine Learning in Healthcare
- Generative Adversarial Networks and Image Synthesis
- Clinical Reasoning and Diagnostic Skills
- Lung Cancer Diagnosis and Treatment
- Ferroelectric and Negative Capacitance Devices
- Machine Learning in Materials Science
- Quantum Computing Algorithms and Architecture
- Medical Imaging and Analysis
- Neuroscience and Neural Engineering
- Force Microscopy Techniques and Applications
- Optimization and Search Problems
- Meta-analysis and systematic reviews
- Robotic Path Planning Algorithms
- Phonocardiography and Auscultation Techniques
Radboud University Nijmegen
2020-2025
Radboud University Medical Center
2020-2025
University of Twente
2020-2025
University Medical Center
2020-2023
Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients chest infections suspected to be caused by COVID-19 using CT may assistance when results from definitive viral testing are delayed. Purpose To develop validate an artificial intelligence (AI) system score likelihood extent pulmonary on scans Reporting Data System (CO-RADS) severity scoring systems. Materials Methods CO-RADS AI...
Multi-agent Pathfinding is a relevant problem in wide range of domains, for example robotics and video games research. Formally, the considers graph consisting vertices edges, set agents occupying vertices. An agent can only move to an unoccupied, neighbouring vertex, finding minimal sequence moves transfer each from its start location destination NP-hard problem. We present Push Rotate, new algorithm that complete problems which there are at least two empty Rotate first divides into...
Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations lack a comprehensive evaluation of accuracy efficiency real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, largest analysis challenge date, benchmark fast, low-resource,...
Artificial Intelligence can mitigate the global shortage of medical diagnostic personnel but requires large-scale annotated datasets to train clinical algorithms. Natural Language Processing (NLP), including Large Models (LLMs), shows great potential for annotating data facilitate algorithm development remains underexplored due a lack public benchmarks. This study introduces DRAGON challenge, benchmark NLP with 28 tasks and 28,824 reports from five Dutch care centers. It facilitates...
Diffusion-based models for text-to-image generation have gained immense popularity due to recent advancements in efficiency, accessibility, and quality. Although it is becoming increasingly feasible perform inference with these systems using consumer-grade GPUs, training them from scratch still requires access large datasets significant computational resources. In the case of medical image generation, availability large, publicly accessible that include text reports limited legal ethical...
The rise of artificial intelligence has led to an explosion in demand for computing power---demand that soon will be insatiable using conventional CMOS-based hardware. Thus there is a worldwide quest unconventional hardware can replace or complement gear. This study contributes simulations how reconfigurable logic realized disordered dopant networks semiconductor. paper reveals the operating principles, based on variable-range hopping charges between randomly located dopants, underlie...
Cine-MRI for adhesion detection is a promising novel modality that can help the large group of patients developing pain after abdominal surgery. Few studies into its diagnostic accuracy are available, and none address observer variability. This retrospective study explores inter- intra-observer variability, accuracy, effect experience. A total 15 observers with variety experience reviewed 61 sagittal cine-MRI slices, placing box annotations confidence score at locations suspect adhesions....
Abdominal adhesions present a diagnostic challenge, and classic imaging modalities can miss their presence. Cine-MRI, which records visceral sliding during patient-controlled breathing, has proven useful in detecting mapping adhesions. However, patient movements affect the accuracy of these images, despite there being no standardized algorithm for defining sufficiently high-quality images. This study aims to develop biomarker determine patient-related factors influence movement cine-MRI....
Abstract We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic and nonlinear classification tasks in dopant network processing units silicon. Kinetic Monte Carlo simulations the process show temperature-dependent current-voltage characteristics, artificial evolution basic Boolean gates, fitness-dependent gate abundances striking agreement with experiment. The provide unique insights local electrostatic potential current flow...
Adhesions are an important cause of chronic pain following abdominal surgery. Recent developments in cine-MRI have enabled the non-invasive diagnosis adhesions. identified on by absence sliding motion during movement. Diagnosis and mapping adhesions improves management patients with pain. Detection is challenging from both a radiological deep learning perspective. We focus classifying presence or sagittal series. experimented spatio-temporal architectures centered around ConvGRU...