جست‌وجوی استاد و پژوهشگر

• Medical Imaging
• Radiation Oncology
• Biomedical Engineering
• +۳ مورد دیگر

Kira Grogg is a Research Scientist in Radiology & Biomedical Imaging at Yale School of Medicine, part of Yale University. She holds affiliations with the Bioimaging Sciences department, the Center for Molecular Imaging Technology and Translation (CMITT), and the PET Core. Her work focuses on advancing medical imaging technologies, particularly in PET imaging and proton therapy verification, alongside studies in pulmonary physiology and lung mechanics. She earned a Ph.D. in Physics from the University of Wisconsin (2011) and a B.A. in Physics and Astronomy from Carleton College (2005). She completed a postdoctoral fellowship at Massachusetts General Hospital/Harvard Medical School, where she investigated proton therapy imaging techniques supported by an NCI K07 Award. Grogg is an ABSNM-certified Nuclear Medicine Physicist and has contributed to over 20 peer-reviewed publications. Her research spans innovations in PET system performance, tumor contouring via AI, and translational studies on mechanical ventilation strategies in lung injury models. She collaborates with institutions like the Yale PET Center and international teams on projects such as the TOPAS-imaging simulation toolkit and tau PET motion correction algorithms. Education: Ph.D., University of Wisconsin (2011); B.A., Carleton College (2005) Key Certifications: ABSNM Diplomate in Nuclear Medicine Physics (2021) Lab Affiliations: Yale PET Center, CMITT Her awards include the NCI K07 Award (2015) and contributions to the development of clinical imaging standards for PET/CT systems. Research highlights include pioneering work on lung strain mechanics in ovine models and applying diffusion models to tumor volume prediction. Current efforts focus on integrating advanced imaging techniques with clinical outcomes in radiation oncology and critical care.

Jianping Luمشاهده پروفایل

استاد

• Medical Imaging
• Radiology
• Biomedical Engineering
• +۳ مورد دیگر

Professor Jianping Lu is a leading academic at the University of North Carolina at Chapel Hill, affiliated with the Department of Physics and Astronomy within the College of Arts and Sciences. His work focuses on advancing medical imaging technologies, particularly in X-ray and computed tomography (CT) systems, with a strong emphasis on carbon nanotube (CNT) X-ray sources. He holds a Ph.D. in Physics from the City University of New York (1988). Education: Ph.D. in Physics, City University of New York, 1988 Research Interests: Development of novel imaging systems, including stationary tomosynthesis and multisource CBCT Optimization of X-ray technology for clinical applications (e.g., oncology, cardiology, dentistry) Integration of artificial intelligence (AI) for diagnostic accuracy and automated analysis Portable and low-cost medical imaging solutions Recent Work Trends: His 2025 publications highlight advancements in AI-driven diagnostics for pancreatic cancer, improved contrast in adaptive radiation therapy, and stationary chest tomosynthesis systems. Key innovations include low-cost dual-energy CBCT and carbon nanotube-based X-ray arrays, which enhance image quality while reducing radiation exposure. His 2024 studies further explore cardiac imaging, dental tomosynthesis, and system optimizations for clinical adoption. Awards: None explicitly listed in the provided text. Advising & Grants: While student advisees are not listed, his research is likely supported by grants focusing on medical imaging innovation. Collaborations span physics, engineering, and clinical departments to bridge technical and clinical challenges. Labs/Teams: Likely affiliated with UNC’s imaging research groups, particularly those developing CNT X-ray technologies and clinical imaging systems for cancer and cardiovascular applications.

Jovan Brankovمشاهده پروفایل

استاد

• Medical Imaging
• Image Sequence Processing
• Pattern Recognition
• +۳ مورد دیگر

Jovan Brankov is a Professor of Electrical and Computer Engineering and Biomedical Engineering at the Illinois Institute of Technology (IIT), within the Armour College of Engineering. He joined IIT in 2002 as a researcher, became Research Assistant Professor in 2004, and was promoted to Assistant Professor in 2008. His research focuses on medical imaging, image sequence processing, pattern recognition, and data mining, with emphasis on biomedical applications such as tumor detection, motion tracking, and tomographic reconstruction methods. Education: Ph.D. in Electrical Engineering from IIT (2002), M.Sc. from IIT (1999), and Dipl. Ing. from the University of Belgrade (1996). Research interests include medical image quality assessment, multiple-image radiography, 4D/5D tomographic reconstruction, and the ADEPT cancer imager. He leads the Advanced X-ray Imaging Laboratory (AXIL) at IIT, which develops phase-sensitive x-ray imaging technologies. Key awards include the IOP Select Award (2006), AIP Editor's Pick (2015), and Nayar Prize phases I (2015) and II (2016). He serves as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and SPIE's Journal of Medical Imaging. His work spans over 150 publications, including peer-reviewed articles and a book chapter. Ongoing projects involve deep learning applications in SPECT imaging, fluorescence-guided surgery, and paired-agent imaging for cancer detection.

Pratik Shahمشاهده پروفایل

استادیار

• Artificial Intelligence in Medicine
• Computational Medicine
• Biomedical Imaging
• +۱۳ مورد دیگر

Dr. Pratik Shah is an Assistant Professor in the Department of Pathology at the University of California, Irvine (UCI) School of Medicine. He leads a cutting-edge research group focused on translational artificial intelligence and biomedical technologies to advance diagnostic imaging, uncover biological mechanisms, and enable clinical deployment of digital therapeutics. Institution: University of California, Irvine School: School of Medicine Department: Department of Pathology Academic Rank: Assistant Professor Email: pratik.shah@uci.edu Dr. Shah's research lies at the intersection of computational medicine, artificial intelligence, and biomedical imaging. His work emphasizes developing interpretable and explainable AI systems that enhance model effectiveness, safety, and clinical utility. Key research areas include generative AI for nondestructive histopathology, deep learning for medical image analysis, AI-driven clinical decision support for infectious diseases, and multimodal imaging for biological insights. His lab is dedicated to creating equitable and responsible technologies to improve diagnosis and treatment of cancer, infectious diseases, and neurological disorders. The recent publications highlight a strong trend in applying deep learning to digital pathology, particularly in computational staining and tumor detection from non-stained images. There is a consistent focus on developing interpretable AI tools for medical image segmentation, with applications in cancer diagnostics, sepsis detection, and dental imaging. The research spans from foundational AI methods to clinical validation, showing a trajectory toward regulatory science and real-world deployment of AI in healthcare. MIT NEWS feature on computational staining research SPIE publication selected for Deep-Dive spotlight session SPIE publication selected for oral presentation IEEE BioInformatics and BioEngineering publication selected for oral presentation Dr. Shah has mentored a diverse group of students and postdoctoral associates, including PhD candidates in Mechanical Engineering at MIT and undergraduate researchers in Electrical Engineering and Computer Science. His trainees have contributed to high-impact publications in journals like JAMA Network and conferences like IEEE EMBC. He has fostered collaborations with institutions including Stanford, UCSF, and the FDA, indicating strong research outreach and interdisciplinary partnerships. His lab has received recognition through media features and conference selections, reflecting the significance and innovation of his work in AI for healthcare. Dr. Shah leads a multidisciplinary research team comprising postdoctoral associates, graduate students, research staff, and student researchers, primarily focused on machine learning and medicine. The lab has strong ties to MIT, where much of the prior research was conducted, and maintains collaborations with clinical and engineering experts across the US.

Carlos Vázquezمشاهده پروفایل

استاد

• Computer vision
• Medical imaging
• 3D reconstruction
• +۴ مورد دیگر

Carlos Vázquez is a Professor at the Department of Software Engineering and IT, École de technologie supérieure (ÉTS). He holds a B.Eng. and M.Sc. from ISPJAE, Cuba, and a Ph.D. from INRS, Montreal. His research focuses on computer vision, medical imaging, 3D reconstruction, and immersive video technologies. He co-leads the Summit Tech Research Chair in Immersive and Interactive Video and is affiliated with the Multimedia Research Laboratory (LABMULTIMEDIA) and the Open Innovation Laboratory in Health Technologies (LIO-ÉTS). His expertise includes stereoscopic imaging, multi-view video coding, and GPGPU programming. Notable research axes are software systems, multimedia, cybersecurity, and health technologies. He has supervised numerous doctoral and master’s theses, including works on 3D spine reconstruction, personalized femur modeling, and immersive video compression. Key contributions include advancements in medical imaging analysis, 3D reconstruction from biplanar radiographs, and efficient video coding for virtual reality. His work bridges clinical applications and engineering, with implications in surgical planning, patient rehabilitation, and multimedia systems optimization.

• Machine Learning
• Distributed Machine Learning
• Medical Signal Processing
• +۳ مورد دیگر

Dr. Mahdi Barhoush is a Postdoc researcher and teaching assistant at RWTH Aachen University since 2023. His work focuses on applying machine learning to medical domains, particularly in distributed learning systems and signal processing. PhD (2023): "Using machine learning in the medical field: speaker signal processing and distributed learning systems" MSc (2015): Communication Engineering, RWTH Aachen University BSc (2013): Telecommunication Engineering, Arab American University His research spans medical machine learning, edge computing, and IoT optimization, with recent publications on federated learning, split learning architectures, and privacy-preserving ECG classification systems. He contributes to advancements in energy-efficient AI for resource-constrained environments and speaker localization in hospitals. Scientific achievements include: Best Paper Award at RADAR 2024 He collaborates with the INDA Institute in Aachen, contributing to active research projects in distributed learning, 6G technologies, and biomedical AI applications.

• Biomedical Engineering
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Burcu Oltu is a Researcher in the Biomedical Engineering Department at Başkent University. She holds both a Master's Degree (2020) and Bachelor's Degree (2017) from the same institution. Her work focuses on applying machine learning techniques to biomedical signal processing and medical imaging. Education: MSc & BSc in Biomedical Engineering, Başkent University Email: boltu@baskent.edu.tr Her research integrates machine learning with biomedical engineering to solve medical diagnostic challenges. Key areas include: Deep learning for pulmonary disease classification from chest X-rays EEG signal analysis for Alzheimer's disease detection Transfer learning approaches for diabetic retinopathy detection Photoplethysmography and blood pressure signal analysis for sleep apnea detection Heart sound signal processing using wavelet transforms Her publications demonstrate a strong focus on developing AI-powered diagnostic tools for various medical conditions, with recent work appearing in journals like BMC MEDICAL IMAGING and Biomedical Signal Processing and Control. She has received recognition for her work, including: Bronze Medal - Istanbul International Invention Fair (ISIF) 2021 Burcu collaborates with researchers like Prof. Dr. Berna Dengiz and Doç. Dr. Selda Güney on projects involving medical imaging analysis and physiological signal processing. Her technical expertise spans algorithm development, neural networks, and biomedical device design.

Kenneth Chenمشاهده پروفایل

پژوهشگر

• Artificial Intelligence in Medicine
• Medical Imaging
• Orthopedics
• +۳ مورد دیگر

Dr. Kenneth Chen is a Researcher affiliated with the University for Continuing Education Krems' Center for Regenerative Medicine. His work focuses on applying artificial intelligence to medical imaging, particularly in orthopedic and spinal diagnostics. Contact him at kenneth.chen@donau-uni.ac.at or via +43 2732 893-5438. Research interests include AI-driven analysis of radiographic data, automated measurement techniques for skeletal structures, and improving diagnostic accuracy through deep learning. His recent projects include analyzing frontal plane leg alignment and Cobb angles in spine radiographs. He is currently involved in the Artificial intelligence in orthopedic radiography analysis project (2022–2025), funded by Austrian federal states. This project emphasizes developing AI tools for orthopedic imaging to enhance clinical decision-making. Dr. Chen collaborates with institutions like the Center for Regenerative Medicine, contributing to interdisciplinary research at the intersection of technology and healthcare. His publications span clinical medicine, biomedical engineering, and computer science, reflecting a commitment to advancing regenerative and precision medicine.

Hannu Aronenمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Neuroimaging
• +۳ مورد دیگر

Hannu Aronen is a Professor and Chair of Radiology at the University of Turku since April 2006, and Chief Radiologist at Turku University Hospital since January 2009. He holds degrees including an M.D. from the University of Helsinki (1983), M.Sc. (Eng) from Helsinki University of Technology (1988), and a Doctor of Medical Sciences (1990). He became a Docent in Radiology (1995) and Functional Medical Imaging (1998). His research focuses on magnetic resonance imaging (MRI) , particularly in neuroimaging and prostate cancer diagnostics. Notable contributions include advancements in perfusion/diffusion MRI for stroke and glioma characterization, and the development of MRI protocols for prostate cancer detection using machine learning. He has led the Functional Brain Imaging Unit (2002–2007) as part of the Helsinki Brain Research Centre, funded by the Academy of Finland. Research Highlights: His work spans MRI methodological innovations, such as susceptibility contrast perfusion and fMRI analysis, and collaborations with institutions like Harvard Medical School and the Turku PET Centre. Over 800 prostate MRI scans have been analyzed in his projects, leading to 17 peer-reviewed articles on prostate cancer imaging accuracy. Current research emphasizes biparametric MRI protocols and AI-driven analysis for tumor detection. Grants & Collaborations: His work has been supported by the Academy of Finland and industry partnerships. Key collaborations include projects with Prof. Leif Østergaard (Aarhus University) on perfusion MRI and Prof. Synnöve Carlson on brain network imaging. Recent studies involve the Turku PET Centre and multinational trials in prostate cancer diagnostics.

• Human Factors Engineering
• Medical Image Perception
• Perceptual Errors
• +۵ مورد دیگر

William F. Auffermann is a Professor at the Department of Radiology and Imaging Sciences, University of Utah, with a dual board certification in Diagnostic Radiology and Clinical Informatics. He serves as Vice Chair for IT and Informatics and Section Chief for Cardiothoracic Imaging, focusing on improving informatics systems and medical education through simulation-based tools. Education: MD and PhD from University of Minnesota Medical School, Fellowship in Cardiothoracic Imaging at Duke University. Board Certifications: American Board of Radiology (Diagnostic Radiology), American Board of Preventive Medicine (Clinical Informatics). His research centers on human factors engineering , integrating medical image perception studies with computer simulation-based education to reduce diagnostic errors. Key areas include biomedical informatics , machine learning , structured reporting , and clinical decision support . Collaborative work spans chest and cardiac imaging with applications in occupational lung disease (NIOSH B-Reader certification). Recent publications emphasize AI integration in radiology , perceptual training , and gaze-display feedback for medical education. He also contributed to ACR Appropriateness Criteria for thoracic imaging and led studies on virtual reality education and gamification in radiology training .

Prateek Prasanaمشاهده پروفایل

استادیار

• Biomedical Imaging
• Computer Vision
• Biomedical Informatics
• +۴ مورد دیگر

Prateek Prasana is an Assistant Professor at Stony Brook University affiliated with the AI Innovation Institute, specializing in bridging human and machine interpretation of biomedical imaging data through computational methods. His academic credentials include: Ph.D. in Biomedical Engineering from Case Western Reserve University M.S. in Electrical and Computer Engineering from Rutgers University B.Tech. in Electrical and Electronics Engineering from National Institute of Technology, Calicut, India Dr. Prasanna's research focuses on developing quantitative tools for lesion growth prediction, disease progression markers, and therapy response signatures in thoracic, neuro, and breast imaging. His work integrates radiomics, pathomics, deep learning, and multi-modal data fusion to create companion diagnostic tools for clinical applications. His 2025 publications reveal dominant trends in diffusion models for medical image synthesis, attention-guided disease classification, and eco-evolutionary pathomic analysis. Key advancements include generative foundation models for histopathology, spatial radiomic graphs for outcome prediction, and gaze-guided frameworks that mimic radiologist visual attention patterns across diverse imaging modalities.

• Radiation Dosimetry
• Health Technology Assessment
• Optimisation
• +۳ مورد دیگر

Dr. Chanchal Kaushik is a Lecturer in Radiography at the University of Salford's School of Health & Society, affiliated with the Centre for Applied Health Research. She joined in 2023 after previous academic positions in India and Bournemouth, bringing 11 years of international experience focused on equity and inclusion in healthcare education. Her research examines: Radiation dosimetry optimization and safety protocols Health technology assessment for diagnostic imaging AI applications for data variability analysis Real-time feedback systems in medical imaging Quality improvement in health services Recent publications (2020-2024) demonstrate consistent focus on medical imaging optimization, radiation dose management, and educational innovation. Work spans technical evaluations of PET-CT systems, patient dose monitoring frameworks, and novel teaching methodologies in radiography education. Awards and recognitions: Active Researcher Excellence Award (2020) £9,300 grant as Co-PI for AI segmentation research (2024-2025) Fellow of Advance HE (2025) Peer reviewer for Radiography Journal and Radiation and Environmental Biophysics Leads research-informed teaching modules across all academic levels in radiography, including Scientific Principles for Diagnostic Radiography, Research Methods, and Masters Dissertation supervision. Contributes to UN Sustainable Development Goals through medical imaging innovation.

Ronald Dendereمشاهده پروفایل

پژوهشگر ارشد

• Health Informatics
• Digital Health
• Medical Imaging
• +۵ مورد دیگر

Dr. Ronald Dendere is a Research Fellow in Health Informatics at the Centre for Health Services Research, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland. His work focuses on health informatics, digital health systems, medical imaging, and disease detection technologies. University of Queensland Centre for Health Services Research Research interests span: Health informatics implementation Digital health maturity impacts Electronic health records in aged care Medical imaging technologies Smartphone-based diagnostics Automated disease detection His publications demonstrate expertise in: Medical imaging simulation Image processing algorithms Radiation dose optimization Healthcare data interoperability Tuberculosis/malaria screening Microscopy automation Available for supervision with a Doctor of Philosophy from University of Cape Town.

Solveig Jülichمشاهده پروفایل

استاد

• History of Medicine
• Medical Ethics
• Media History
• +۴ مورد دیگر

Solveig Jülich is Professor of History of Ideas and Scholarship at Uppsala University's Department of History of Ideas within the Faculty of Arts. Since joining Uppsala in 2014, she has held leadership positions including Deputy Head of Department (2017-2020) and Chair of the steering committee for the Centre for Medical Humanities. She earned her PhD from Linköping University in 2002, and previously held academic positions at Linköping University and Stockholm University. Her research focuses on the media history of medicine , historical perspectives on medical ethics , and democratization processes in science . Key research areas include: Visual culture of pregnancy and fetal representation Historical bioethics and value conflicts Medical communication and public engagement Swedish medical history and policy evolution Analysis of her publications reveals consistent focus on visual culture in medicine , particularly fetal imaging and representation, with strong interdisciplinary connections to ethics, history, and media studies. Her work frequently examines tensions between scientific progress and societal values in Swedish medical contexts. She leads significant research projects funded by national organizations: Medicine's Communication: Digitalization of Swedish Medical Journals, 1781–2011 (SweMPer, 2023-2026) Between Openness and Secrets: Control of Medical Communication in 20th Century Sweden (2023-2025) Medicine at the Borders of Life (2015-2021) Photography of Life and Death: Lennart Nilsson, Medicine and Media in Sweden (2022) She serves in numerous academic leadership roles including: External Board Member, Ridderstad Foundation (2024–) Advisory Board Member, Center for the History of Science (2023–) Editorial Board Member, European Journal for the History of Medicine and Health (2020–) Chair of Uppsala University's Forum for Medical Humanities and Social Sciences (2017-2019)

Nicole McFarlaneمشاهده پروفایل

دانشیار

• Electrical Engineering
• Microelectronics
• Biosensors
• +۳ مورد دیگر

Nicole McFarlane is an Associate Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UTK), where she also holds the TCE Advance Professor title. She is affiliated with the Tickle College of Engineering and leads the Micro-Bio-Electronics Group (MLAB), focused on advanced sensor technologies. Education: Nicole McFarlane earned a PhD in Electrical Engineering from the University of Maryland, College Park (2010), and dual bachelor's (BS, 2001) and master's (MS, 2003) degrees in Electrical Engineering from Howard University, Washington D.C. Research Interests: Her work centers on developing compact, efficient sensor systems using mixed-signal CMOS strategies and nanotechnology. Applications span electrochemical biosensors, point-of-care medical devices, nuclear imaging panels, and hardware security for embedded systems. The MLAB lab emphasizes cost-effective, scalable solutions for healthcare, military, and research domains. Article Trends: Recent publications (2024) highlight innovations in perimeter-gated SPAD arrays for cryptographic security, silicon photomultipliers for nuclear imaging, and asynchronous readout architectures. 2023 work includes multimodal gesture recognition systems and pH sensors with integrated encryption. These studies reflect her focus on merging cutting-edge CMOS technologies with secure, multifunctional sensor design. Scientific Awards: No awards explicitly listed in the provided texts. Advising & Grants: Nicole advises students in the Kao Department's graduate and undergraduate programs. While specific advisee names are not provided here, her research is supported by grants in microelectronics and sensor integration. She has pioneered portable biosensor systems, including wireless pH-impedance devices and encryption architectures for wearable health monitors. Labs & Teams: As founder/leader of the Micro-Bio-Electronics Group (MLAB), she oversees projects combining CMOS fabrication with nano/micro-fabrication techniques. The lab explores applications ranging from implantable medical sensors to battlefield-ready detection systems, emphasizing interdisciplinary collaboration.