A5075792402- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Lung Cancer Diagnosis and Treatment
- Medical Imaging and Pathology Studies
- Traumatic Ocular and Foreign Body Injuries
- Trauma Management and Diagnosis
- Artificial Intelligence in Healthcare and Education
- Prostate Cancer Treatment and Research
- Glaucoma and retinal disorders
- Advanced MRI Techniques and Applications
- Retinal and Optic Conditions
- Pediatric Urology and Nephrology Studies
- Retinal Diseases and Treatments
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Glioma Diagnosis and Treatment
- Renal and Vascular Pathologies
- Neural Networks and Applications
- Atomic and Subatomic Physics Research
- Generative Adversarial Networks and Image Synthesis
- Stock Market Forecasting Methods
- Digital Media Forensic Detection
- Medical Imaging and Analysis
- Anomaly Detection Techniques and Applications
- Music and Audio Processing
- Neural Networks and Reservoir Computing
Radboud University Nijmegen
2020-2024
Radboud University Medical Center
2020-2024
University Medical Center
2020-2023
University of Groningen
2017-2018
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...
Fish are able to sense water flow velocities relative their body with mechanoreceptive lateral line organ. This organ consists of an array detectors distributed along the fish body. Using excitation these individual detectors, can determine location nearby moving objects. Inspired by this sensory modality, it is shown here how neural networks be used extract object's from simulated patterns, as measured arrays stationary artificial velocity sensors. The applicability, performance and...
Challenges drive the state-of-the-art of automated medical image analysis. The quantity public training data that they provide can limit performance their solutions. Public access to methodology for these solutions remains absent. This study implements Type Three (T3) challenge format, which allows on private and guarantees reusable methodologies. With T3, organizers train a codebase provided by participants sequestered data. T3 was implemented in STOIC2021 challenge, with goal predicting...
Amidst the ongoing pandemic, assessment of computed tomography (CT) images for COVID-19 presence can exceed workload capacity radiologists. Several studies addressed this issue by automating classification and grading from CT with convolutional neural networks (CNNs). Many these reported initial results algorithms that were assembled commonly used components. However, choice components was often pragmatic rather than systematic systems not compared to each other across papers in a fair...
Total lung volume is an important quantitative biomarker and used for the assessment of restrictive diseases.In this study, we investigate performance several deep-learning approaches automated measurement total from chest radiographs.About 7621 posteroanterior lateral view radiographs (CXR) were collected patients with CT available. Similarly, 928 CXR studies chosen pulmonary function test (PFT) results. The reference was calculated segmentation on or PFT data, respectively. This dataset to...
Abstract Background Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use CT in screening, diagnosis, and staging restrictive pulmonary diseases. Estimating lung per lobe, which cannot be done with PFTs, would helpful for risk assessment resection surgery bronchoscopic volume reduction. Purpose To automatically estimate PFT furthermore disentangle individual contribution lobes to a patient's function. Methods We propose I3Dr, deep...
Amidst the ongoing pandemic, several studies have shown that COVID-19 classification and grading using computed tomography (CT) images can be automated with convolutional neural networks (CNNs). Many of these focused on reporting initial results algorithms were assembled from commonly used components. The choice components was often pragmatic rather than systematic. For instance, 2D CNNs even though might not optimal for handling 3D CT volumes. This paper identifies a variety increase...
Challenges drive the state-of-the-art of automated medical image analysis. The quantity public training data that they provide can limit performance their solutions. Public access to methodology for these solutions remains absent. This study implements Type Three (T3) challenge format, which allows on private and guarantees reusable methodologies. With T3, organizers train a codebase provided by participants sequestered data. T3 was implemented in STOIC2021 challenge, with goal predicting...