جست‌وجوی استاد و پژوهشگر

Peter de Withمشاهده پروفایل

استاد

• Video Compression
• Image Analysis
• Healthcare Applications
• +۴ مورد دیگر

Peter de With is a Part-time Full Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), leading the Video Coding & Architectures Group. His expertise spans video compression, image analysis in healthcare, surveillance, and multimedia. He holds a PhD from Delft University of Technology (1992) and has held roles at Philips Research, the University of Mannheim, and Cyclomedia Technology. His research focuses on video/image signal processing, machine learning, and healthcare applications such as esophageal cancer detection. He is an IEEE Fellow and has authored over 400 papers, receiving multiple awards for his work. Research interests include video coding, medical imaging, and system innovations in health, surveillance, and automotive sectors. Key publications address challenges in medical instrument detection, 3D ultrasound, and multiview rendering. He collaborates with industry partners like Bosch Security and hospitals for oncology research. His work bridges academic innovation with real-world applications, emphasizing practical solutions in healthcare technology and multimedia systems. Awards include IEEE Fellow status and paper awards from CE Chester Sall, SPIE, and Elsevier. He chairs program committees for IEEE conferences and holds 30 patents. His contributions to video analysis and system design have driven advancements in safety, security, and healthcare technologies.

• Artificial Intelligence
• Health Informatics
• Knowledge Graphs
• +۴ مورد دیگر

Prof. Zhisheng Huang is a Visiting Professor at the Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on AI-driven solutions for healthcare challenges, particularly leveraging knowledge graphs for mental health analysis, biomedical informatics, and social media monitoring. He actively contributes to UN Sustainable Development Goals related to health and well-being (SDG 3). Key Research Areas: Knowledge graphs for dietary nutrition and disease analysis Suicide risk detection via social media (e.g., "Tree Hole" project) Medical Q&A systems for geriatric diseases AI applications in mental health diagnostics His work integrates ontology engineering, machine learning, and big data analytics to address complex health issues. Notable projects include the KG4NH knowledge graph and hypertension early warning systems. Collaborations span international institutions, focusing on interdisciplinary health informatics solutions. Recent Focus: Exploring microbiota-gut-brain axis relationships, depression etiology, and family dynamics in postpartum mental health. His research bridges computational methods with clinical practice to improve healthcare outcomes.

• Medical Robotics
• Biomechanical Modeling
• Medical Imaging
• +۳ مورد دیگر

Ferdinand van der Heijden is an Associate Professor in Robotics and Mechatronics, specializing in medical robotics and surgical navigation. He holds an external researcher position at Antoni Van Leeuwenhoek Hospital since 2014. His work integrates robotics, biomedical engineering, and artificial intelligence to address challenges in surgical precision, diagnostic imaging, and patient outcomes. Research focuses include developing advanced imaging techniques for pleural diseases, improving laparoscopic camera accuracy, and leveraging AI for medical diagnostics. He has contributed to over 160 publications, with recent emphasis on pleural plaque quantification, crowd-sourced deep learning for hemorrhage detection, and personalized surgical models. He has supervised 11 academic works, though specific student names are not listed. His collaborations span global institutions, addressing topics like diabetic foot thermography and prostate intervention robotics. Notable projects include the MIRIAM robot for MR-guided needle interventions and biomechanical tongue modeling for post-surgery functional prediction. Key contributions include innovations in image-guided surgery tools, AI-driven diagnostic frameworks, and 3D visualization techniques for surgical planning. His work aligns with UN Sustainable Development Goals related to health and well-being (SDG 3).

• Artificial Intelligence
• Reinforcement Learning
• Crop Management
• +۳ مورد دیگر

Michiel Kallenberg is a Postdoctoral Researcher in Artificial Intelligence, focusing on applications in agricultural management and medical imaging. His work bridges reinforcement learning, machine learning, and domain adaptation with practical applications in crop yield prediction, nitrogen optimization, and breast cancer risk assessment. Research Areas: AI for agriculture, medical imaging, reinforcement learning Collaborations: Involved in interdisciplinary projects like 'Artificial Intelligence for reducing fertilizer and pesticide use' and 'Digital Future Farm' Recent publications highlight his expertise in constrained reinforcement learning for precision agriculture and volumetric breast density estimation using deep learning. His research trends show a dual focus on environmental sustainability and healthcare diagnostics. He co-supervises PhD candidates and collaborates with institutions on datasets like CY-Bench, which provides benchmarking for subnational crop yield forecasting. His projects emphasize integrating data-driven approaches with process-based models.

• Gastroenterology
• Hepatology
• Colorectal Cancer
• +۷ مورد دیگر

Fanny Vuik is a researcher at Erasmus MC, specifically within the Department of Gastroenterology & Hepatology in the Faculty of Medicine. Her work centers on colorectal diseases, cancer screening, and advanced diagnostic techniques such as colon capsule endoscopy. Her research interests include: Colorectal cancer and early detection Applications of artificial intelligence in endoscopic imaging Systematic reviews and meta-analyses in gastroenterology Gastrointestinal transit dynamics Prevalence of gastrointestinal abnormalities in population studies Her recent publications demonstrate a strong focus on clinical research, particularly in synthesizing evidence through systematic reviews and exploring novel technologies for improving diagnosis. The trend in her work shows increasing integration of AI with traditional endoscopic methods, aiming to enhance accuracy and accessibility in colorectal cancer screening. She has contributed to high-impact journals such as JAMA Network Open , Diagnostics , and Clinical Gastroenterology and Hepatology . Her collaborations involve leading experts in gastroenterology, including E. J. Kuipers and M. C. W. Spaander. Fanny Vuik completed her doctoral thesis in 2022 titled The gastrointestinal tract: From healthy mucosa to colorectal cancer , which reflects her deep engagement with the pathophysiology of colorectal disease. She has no listed scientific awards or known advisees in the provided text. There is no information about grants or labs she leads, though her research output suggests active participation in collaborative clinical research projects.

• Radiomics
• Artificial Intelligence in Radiology
• Medical Imaging
• +۷ مورد دیگر

Jan-Jaap Visser is a researcher in the Department of Radiology & Nuclear Medicine at Erasmus MC, Rotterdam, Netherlands. He is actively contributing to the advancement of medical imaging through research in radiomics, artificial intelligence, and diagnostic imaging technologies. His research interests center on the application of artificial intelligence in radiology, with a focus on radiomics, MRI, CT imaging, and computer-aided diagnosis systems. He explores how AI can improve diagnostic accuracy, support clinical decision-making, and enhance patient understanding of radiological findings. Recent publications highlight his work in deep learning for brain metastasis analysis, GPT-4 for patient comprehension of radiology reports, AI in musculoskeletal tumor imaging, radiomics practice guidelines, and validation of commercial CAD systems for lung nodule detection. These works reflect a strong trend toward integrating AI into clinical radiology workflows with attention to validation and standardization. No scientific awards were mentioned in the provided text. Jan-Jaap Visser has supervised academic work, as indicated by the 'Supervised Work (2)' tag, though specific student names are not listed. He collaborates extensively with researchers across institutions and contributes to national and international initiatives in radiological informatics. There is no mention of specific grants, but his publication output suggests active research funding. He is involved in research networks related to radiomics and AI in imaging, likely participating in collaborative teams focused on medical image analysis and informatics at Erasmus MC.

Myrthe Plaisierمشاهده پروفایل

استادیار

• Multisensory Technology
• Sensory Substitution
• Haptic Perception
• +۵ مورد دیگر

Dr. Myrthe Plaisier is an Assistant Professor at the Human Technology Interaction group within the Industrial Engineering and Innovation Sciences school at Eindhoven University of Technology . She holds a PhD in Experimental Physics from Utrecht University (2010) and conducted postdoctoral work at Bielefeld University and Vrije Universiteit Amsterdam. Research Interests : Multisensory technology for sensory substitution Haptic perception and tactile feedback systems Assistive technology for deafblind individuals Audiovisual temporal integration in virtual environments Vibrotactile spatial acuity Article Trends : Her research spans haptic device design, spatial perception studies, sensory substitution methodology, and tactile communication frameworks. Recent works focus on outdoor virtual reality synchronization, wearable navigation aids, and vibrotactile co-design tools for accessibility. Scientific Awards : 2010: Thesis award from Dutch Psychonomics Society 2011: NWO Rubicon grant 2013: NWO VENI grant 2024: Horizon 2020 Marie Sklodowska-Curie grant #812719 Grants & Collaborations : She has secured multiple competitive grants including NWO VENI and Rubicon, and participates in European consortiums like SUITCEYES. Her work involves cross-disciplinary collaboration with institutions across Germany and the Netherlands.

Ahmed Elazabمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۷ مورد دیگر

Ahmed Elazab serves as an Associate Researcher at Shenzhen University's School of Biomedical Engineering since January 2021, following a Postdoctoral Research Fellowship at the same institution from January 2018 to April 2020. He holds a Ph.D. in Pattern Recognition and Intelligent Systems from the Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences (2017). Education Ph.D. in Pattern Recognition and Intelligent Systems, Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences, China (2017) Research Focus Dr. Elazab's work centers on machine learning and deep learning applications in biomedical contexts, with specialized expertise in medical image analysis , brain anatomy analysis , and computer-aided diagnosis . His research integrates computer vision, bioinformatics, and data science to develop AI-driven solutions for complex medical challenges including neurodegenerative diseases and infectious outbreaks. Publication Trends Analysis of his recent publications (2020-2023) reveals a concentrated focus on deep learning for medical diagnostics, particularly in Alzheimer's disease staging from MRI data and COVID-19 detection from X-ray images. His work consistently incorporates domain-specific knowledge into neural architectures, with growing emphasis on generative models for medical image segmentation and vaccine development. Cross-cutting themes include handling multi-modal biomedical data and addressing real-world clinical constraints. Scientific Recognition Active Academic Editor for PeerJ Computer Science with 1,600 contribution points Reviewer for prestigious international journals across computer science and biomedical domains Author/co-author of over 80 peer-reviewed publications Academic Engagement Dr. Elazab maintains significant scholarly involvement through editorial work at PeerJ Computer Science, where he has handled manuscripts on deep learning applications in medical imaging since 2020. His extensive publication record demonstrates consistent research productivity, though specific grant funding or student supervision details are not documented in available sources. He contributes to multiple subject areas including Artificial Intelligence, Bioinformatics, and Computational Biology. Research Environment As part of Shenzhen University's School of Biomedical Engineering, Dr. Elazab operates within a multidisciplinary research ecosystem focused on AI-driven medical solutions. His work intersects with ongoing initiatives in medical image computing and computational diagnostics, leveraging institutional resources for biomedical data analysis without specified laboratory affiliations.

Harry Hochheiserمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Information Visualization
• Bioinformatics
• +۷ مورد دیگر

Harry Hochheiser is an Assistant Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine, where he also serves as Associate Director of the Biomedical Informatics Training Program. His academic career spans multiple disciplines at the intersection of computing and healthcare, with a strong focus on human-computer interaction, bioinformatics, and medical informatics. Dr. Hochheiser's research interests include human-computer interaction, information visualization, bioinformatics, universal usability, security, privacy, and public policy implications of computing systems. His work particularly focuses on NIH-funded projects related to bioinformatics research portals, visualization for review of chart records, and tools for aiding the discovery of animal models of human diseases. His research demonstrates a commitment to developing technology that addresses real clinical needs while considering user experience and ethical implications. His publication record shows consistent scholarly output across multiple domains, with recent work emphasizing biomedical informatics, machine learning applications in healthcare, epidemiological modeling, and academic publishing practices. His research demonstrates a strong interdisciplinary approach that bridges computer science with clinical medicine and public health, with particular attention to human-centered design principles in healthcare technology. Editorial Board Member, PeerJ - the Journal of Life & Environmental Sciences Editorial Board Member, PeerJ Computer Science Over 2,900 points on PeerJ representing 2,800 editorial contributions and 105 reviews Dr. Hochheiser has made significant contributions across numerous subject areas including Science and Medical Education, Science Policy, Statistics, Human-Computer Interaction, Computational Science, Bioinformatics, and many others. His work has practical implications for healthcare technology development, particularly in creating more effective, user-friendly systems that address real clinical needs while considering privacy and security concerns.