جست‌وجوی استاد و پژوهشگر

Philipp Lohmannمشاهده پروفایل

استاد پژوهشی

• Medical Imaging
• Neuro-Oncology
• PET Imaging
• +۳ مورد دیگر

Priv.-Doz. Dr. Philipp Lohmann is a Research Professor at the Research Center Jülich within the Institute of Neuroscience and Medicine (INM) , specifically in the Physics of Medical Imaging (INM-4) department. His work focuses on advancing neuro-oncological imaging techniques, particularly PET and MRI, for brain tumor diagnosis, treatment monitoring, and prognosis prediction. He leads initiatives in radiomics, artificial intelligence applications in medical imaging, and interdisciplinary tumor board collaborations. Education: Not explicitly detailed in the provided text. Key research areas include: PET/MRI integration for brain tumor characterization Development of AI-driven imaging segmentation tools Validation of FET PET for glioma recurrence detection Radiomics-based biomarker discovery for treatment response prediction Impact of brain lesions on cognitive outcomes in glioma patients His recent work emphasizes clinical translation of imaging advancements, including cost-effectiveness analyses and multicentric trials to standardize FET PET use. He actively participates in global neuro-oncology initiatives (e.g., EANO Career Boost) and imaging challenges (e.g., BraTS-METS).

• Federated Learning
• Decentralized AI
• Blockchain Technology
• +۴ مورد دیگر

Dr. Muhammad Habib Ur Rehman is a Senior Lecturer in Computer Science at the University of Bedfordshire's Faculty of Creative Arts, Technologies & Science, within the School of Computer Science and Technology. His research focuses on federated learning in healthcare, blockchain technologies, privacy-preserving AI, and distributed systems. He holds a PhD in Distributed Computing (2017) and has expertise in teaching programming, artificial intelligence, and distributed systems. Research Interests : Federated Learning for Medical Imaging and Healthcare Decentralized AI and Blockchain Integration Privacy-Enhancing Technologies (PETs) Edge Computing and IoT Security Publications : Over 40 peer-reviewed articles on federated learning frameworks, blockchain applications, and IoT security. Notable works include: Privacy-preserving federated learning for radiology Blockchain-based trust systems for cryptocurrencies Edge computing solutions for smart cities Affiliations : Senior Member of IEEE. Active in research institutes like the Institute for Research in Applicable Computing (IRAC). Contact : muhammadhabibur.rehman@beds.ac.uk

Ai-Chi Chienمشاهده پروفایل

استاد

• Intracranial Aneurysm Research
• Cerebrovascular Circulation
• Medical Imaging
• +۵ مورد دیگر

Ai-Chi Chien is a Professor-in-Residence in the Department of Radiological Sciences at the David Geffen School of Medicine at UCLA. With a robust research program focused on cerebrovascular diseases, Dr. Chien directs the Chien Lab which specializes in the intersection of medical imaging, hemodynamics, and computational analysis of vascular pathologies, particularly intracranial aneurysms and cerebrovascular circulation. Dr. Chien's research interests span multiple domains of medical imaging and vascular analysis, with primary focus on intracranial aneurysm growth prediction, cerebrovascular hemodynamics, and the application of artificial intelligence to radiological imaging. His work integrates computational fluid dynamics with advanced imaging techniques to develop predictive models for aneurysm behavior and rupture risk. More recently, Dr. Chien has expanded his research into AI applications for radiology report analysis and medical imaging interpretation, bridging traditional vascular imaging with modern machine learning approaches. The publication record reveals a clear evolution from foundational work in cerebrovascular imaging and aneurysm hemodynamics toward increasingly sophisticated integration of artificial intelligence and machine learning techniques. Early research focused on quantitative analysis of aneurysm morphology and hemodynamics, while recent publications demonstrate a strategic pivot toward AI-assisted medical imaging analysis, including natural language processing for radiology reports and deep learning for disease diagnosis. This trajectory reflects both the maturation of computational methods in medical imaging and Dr. Chien's adaptability in incorporating emerging technologies into vascular research. Active member of UCLA's Graduate Program in Bioscience Principal Investigator for multiple NIH-funded research projects Regular collaborator with neurosurgery, radiology, and biomedical engineering departments Established investigator in cerebrovascular imaging with over 15 years of continuous publication record Dr. Chien maintains an active laboratory environment with ongoing projects spanning from basic vascular imaging research to clinical applications of AI in radiology. The Chien Lab serves as a hub for interdisciplinary collaboration between clinicians, engineers, and computer scientists, fostering innovation at the intersection of vascular medicine and computational analysis.

• Cardiovascular Imaging
• Cardiac Electrophysiology
• Heart Failure
• +۳ مورد دیگر

Dr. Jerold Shinbane is a Clinical Professor at the Keck School of Medicine of the University of Southern California , where he serves as Director of the USC Arrhythmia Center . His expertise spans cardiovascular imaging, cardiac electrophysiology, and device therapies for heart failure. Board-certified in Internal Medicine, Cardiovascular Disease, and Clinical Cardiac Electrophysiology Fellow of the American College of Cardiology, Heart Rhythm Society, and Society of Cardiovascular Computed Tomography Co-editor of three textbooks on Cardiac Computed Tomography Research focuses on advanced cardiovascular imaging techniques (CT and MRI) for diagnosing and planning treatments for heart rhythm disorders. Recent publications emphasize Takotsubo Syndrome epidemiology, machine learning mortality prediction, and surgical applications of virtual patient avatars using CT/MRI. Key subfields include device therapies for heart failure , congenital heart disease imaging , and multisystem interactions in cardiovascular pathologies. His work has been published in journals such as European Journal of Clinical Investigation , Journal of the American College of Cardiology , and Heart Rhythm . Scientific Honors Fellow of the American College of Cardiology Fellow of the Heart Rhythm Society Fellow of the Society of Cardiovascular Computed Tomography Publications demonstrate interdisciplinary innovation combining cardiac electrophysiology with advanced imaging and virtual reality applications in cardiovascular medicine.

• Diffusion MRI
• Medical Physics
• Neuroimaging
• +۳ مورد دیگر

Dr. Chantal Tax is a Research Fellow at Cardiff University's School of Physics and Astronomy, specializing in diffusion MRI and its applications in neuroimaging. Her work focuses on advancing quantitative MRI techniques, including ultra-strong gradient diffusion MRI and tractography, to study brain microstructure, white matter abnormalities, and developmental differences in preterm infants. Tax has contributed significantly to methodological advancements in diffusion MRI preprocessing, harmonization, and clinical applications in oncology and neurology. Her research interests span medical physics, neurodevelopmental imaging, and the integration of machine learning for optimizing MRI protocols. Notable studies include exploring myelin sheath quantification via diffusion models, assessing cancer microstructure, and investigating white matter changes in movement disorders. Tax collaborates extensively with global institutions, contributing to open challenges in diffusion MRI metadata standards (e.g., aDWI-BIDS) and reproducibility in tractography. Publications highlight Tax's expertise in diffusion MRI's clinical and research applications, including pediatric neuroimaging, brain connectivity mapping, and the development of novel imaging biomarkers. Her work bridges physics, computer science, and clinical medicine, addressing challenges in neuroimaging accuracy and accessibility.

• Cancer Research
• Medical Imaging
• Radiomics
• +۴ مورد دیگر

Hugo Aerts is a Full Professor at Maastricht University's Faculty of Health, Medicine and Life Sciences. He holds affiliations with Maastricht University Medical Center+ (MUMC+) and the GROW School for Oncology and Developmental Biology. His research focuses on cancer imaging, radiomics, and AI-driven healthcare solutions. Aerts leads projects in deep learning applications for radiation therapy quality assurance, biomarker development using medical imaging, and systemic immune dysregulation in aging and cancer. Key research areas include radiomics, oncological imaging, and translational cancer biology. His recent work explores epicardial adipose tissue as a mortality predictor, sarcopenia's role in lung cancer outcomes, and biological age estimation via facial photographs. He has supervised 9 research works and contributed over 148 publications, with notable impact in high-impact journals like The Lancet Digital Health and Nature Genetics. Aerts collaborates with global networks such as the TRACERx Consortium and Genomics England, driving advancements in precision oncology and immune system analysis. His lab integrates multi-disciplinary approaches combining radiology, machine learning, and molecular biology to address clinical challenges in cancer diagnosis and treatment.

• Nuclear Nonproliferation
• Radiation Detection
• Fission Data
• +۴ مورد دیگر

Adam Hecht is a Professor of Nuclear Engineering at the University of New Mexico (UNM), leading research in radiation detection, nuclear nonproliferation, and fission data. He holds a Ph.D. from Yale University (2004) and has held academic positions since 2008, progressing from Assistant to Associate Professor before becoming full Professor in 2022. His work focuses on advanced detector development (e.g., AlSb semiconductors, perovskites), muon imaging for spent fuel verification, and machine learning applications in radiation spectroscopy. He directs the UNM Radiation Research and Detector Development (R²D²) lab and co-leads the Center for Nuclear Nonproliferation Science and Technology (CNNST). Education: Ph.D. Physics (Yale, 2004), M.Phil. (Yale, 2001), M.S. (Yale, 1998), B.S. Physics (UC Irvine, 1997). Research emphasizes fission fragment measurements, nonproliferation technologies, and environmental radiation analysis. He has authored/co-authored over 70 peer-reviewed articles, with recent work on neutron multiplicity, detector materials, and muon tomography for nuclear safeguards. Research interests span experimental nuclear physics, including SPIDER fission spectrometry, novel detector materials (AlSb, perovskites), and radiation data mining. Awards include the J.W. Gibbs Fellowship (Yale). Collaborations include LANL, UNLV, and the Canadian Penning Trap mass spectrometry projects. Students and postdocs in his lab work on projects such as fission yield measurements, neutrino detection, and neutron/gamma identification algorithms. Key contributions include the development of the UNM Fission Fragment Spectrometer and advancements in room-temperature semiconductor detectors. His work bridges fundamental nuclear science with applied security needs, including projects funded by DOE, DTRA, and NSF.

• Medical Artificial Intelligence
• Machine Learning in Biomedical Imaging
• Prostate Cancer Diagnosis
• +۴ مورد دیگر

Dr. John Onofrey is an Assistant Professor of Radiology & Biomedical Imaging and Urology at Yale School of Medicine. He holds a PhD in Biomedical Engineering from Yale and prior computer science degrees from Johns Hopkins University. His research focuses on developing AI-driven solutions for clinical challenges in prostate and liver cancer diagnostics, PET imaging, and medical image analysis. He leads NIH-funded projects and co-teaches an interdisciplinary course on data-driven clinical decision-making. His work emphasizes machine learning applications in medical imaging, including segmentation, motion correction, and predictive modeling. Notable contributions include automated liver segmentation algorithms, bias analysis in medical AI, and prostate cancer risk mapping using MRI. He collaborates with departments like Biomedical Engineering and the Center for Biomedical Data Science. Publications span journals like European Radiology and IEEE Transactions on Medical Imaging, with a focus on translational AI solutions. His lab (OnofreyLab) actively develops open-source tools for biomedical imaging tasks.

• Biomedical Engineering
• Cryoelectron Microscopy
• Magnetic Resonance Imaging
• +۷ مورد دیگر

Dr. Hemant D. Tagare serves as Professor of Radiology and Biomedical Imaging and of Biomedical Engineering at Yale School of Medicine. He maintains a primary appointment in Radiology & Biomedical Imaging and secondary appointment in Statistics, with extensive affiliations across Yale's research ecosystem including the Bioimaging Sciences Center, Center for Brain & Mind Health, Computational Biology and Biomedical Informatics, Image Processing & Analysis Group, and Yale Biomedical Imaging Institute. With a PhD from Rice University (1989), Dr. Tagare's research program addresses fundamental challenges in biomedical image analysis across four core areas: image segmentation (particularly cardiac ultrasound and brain MRI), non-rigid registration (developing axiomatic frameworks for brain deformation analysis), 3D protein structure reconstruction from Cryogenic Electron Microscopy, and shape theory (exploring topology and geometry of affine shape spaces). His work integrates machine learning and advanced numerical optimization techniques to develop innovative methodologies that advance medical imaging capabilities. Dr. Tagare's recent publications demonstrate continued productivity in cryo-EM particle picking consensus methods, MRI standardization techniques, perfusion measurement in vascular disease, and parameter mapping algorithms. His research program, supported by NIH funding, consistently bridges theoretical developments with practical clinical applications across biomedical imaging domains. Honors: Best poster award for "Tunnelling Descent: A New Algorithm for Active Contour Segmentation of Ultrasound Images" (2003) Francois Erbsmann Award (Honorable Mention) for outstanding paper in medical image processing (1993) National Science Talent Search Scholarship from Government of India (1977) Dr. Tagare maintains active collaborations with Yale researchers including Gigi Galiana, Dana Peters, Mark Gerstein, Pradeep Uchil, Todd Constable, and Walther Mothes. His work spans computational biology, medical physics, and clinical applications, demonstrating the interdisciplinary nature of modern biomedical imaging research that bridges engineering principles with clinical medicine.

• Medical Imaging
• Deep Learning
• Neural Networks
• +۳ مورد دیگر

Anne-Marie Rickmann is a Research Fellow in the Department of Radiology & Biomedical Imaging at Yale University's Yale School of Medicine. Her research focuses on advancing medical imaging techniques through deep learning, with a particular emphasis on image segmentation, cortical surface reconstruction, and domain adaptation in healthcare contexts. Her work integrates cutting-edge methodologies such as neural deformation fields, diffeomorphic mesh deformations, and foundation models to address challenges in MRI/CT analysis, neuroimaging, and abdominal organ segmentation. She explores applications in pre-clinical cardiology, longitudinal cortex analysis, and post-surgical imaging, emphasizing faithful AI explanations and adaptive learning frameworks. Notable contributions include the development of tools like AbdomenNet, V2C-Long, and STRUDEL, which enhance precision in medical imaging while addressing issues like hallucination-free segmentation and uncertainty quantification. Her publications reflect a strong focus on interdisciplinary approaches at the intersection of AI, biomedical engineering, and clinical practice. No scientific awards or grants are explicitly mentioned in the provided materials. She collaborates within the Department of Radiology & Biomedical Imaging, contributing to projects that bridge computational methods with clinical diagnostics and epidemiologic studies.

Carole Frindelمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Medical Imaging
• +۷ مورد دیگر

Carole Frindel is an Assistant Professor at INSA Lyon, affiliated with the Biomedical Imaging Research Laboratory (CREATIS), and also holds a position at the Graduate School of Biomedical Engineering, Tohoku University, since 2020. Her work focuses on machine learning, deep learning, and privacy-preserving AI applications in neuroimaging and medical imaging. Education: M.Sc. in Computer Vision (Ecole Polytechnique de Montréal, 2005), M.Sc. in Bioinformatics (INSA Lyon, 2006), Industrial Ph.D. in Electrical and Computer Engineering (INSA Lyon & Siemens Healthineers, 2009) Her research addresses major health challenges through advanced computational methods, including neuroimaging , signal/image processing , image synthesis , and statistical learning , with a strong emphasis on privacy in AI . Recent projects like ANR IMAGE-TEXT-AVC and FIL MEDIKG highlight her leadership in multimodal data fusion and ethical AI development. The 15 most recent publications reflect trends in stroke outcome prediction , tractography , diffusion MRI , and privacy-preserving healthcare AI . Collaborations with institutions like Siemens Healthineers, French National Research Agencies (ANR), and international conferences (MICCAI, ISBI) underscore her interdisciplinary impact. 2025: Junior Member, Institut Universitaire de France 2018: Bronze Award, ISMRM 2007: Ph.D. Scholarship, Rhône-Alpes region Frindel supervises numerous Ph.D. and Master’s students, with a focus on stroke lesion analysis, computational fluid dynamics, and privacy in medical AI. Her grants include ANR IMAGE-TEXT-AVC (Principal Investigator) and RHU BOOSTER (Local PI). Frindel leads the Neuroimaging Working Group at CREATIS and co-organizes conferences like MIDL. She also contributes to editorial boards and peer review for journals and conferences in medical imaging and AI.

• Medical Imaging
• Radiation Therapy
• Deep Learning
• +۳ مورد دیگر

Dr. Jason Dowling is an Adjunct Associate Professor at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing medical imaging and artificial intelligence techniques for radiation therapy applications, particularly in MRI-based treatment planning and synthetic CT generation. He leads interdisciplinary projects involving deep learning, medical image segmentation, and clinical decision support systems. His work integrates computational methods with clinical needs, addressing challenges in prostate cancer radiotherapy, cervical cancer imaging, and forensic applications of medical imaging. Key contributions include developing algorithms for MR-to-CT synthesis, automated tumor and organ segmentation, and uncertainty quantification in clinical datasets. Dr. Dowling’s research also explores the application of generative adversarial networks (GANs) and probabilistic models to improve treatment planning accuracy and reduce reliance on traditional CT scans. His collaborations span academic and clinical institutions globally, driving advancements in precision medicine and AI-driven healthcare solutions.

Shaheen Dewjiمشاهده پروفایل

استادیار

• Computational Dosimetry
• Radiation Detection
• Nuclear Safety
• +۴ مورد دیگر

Dr. Shaheen Dewji is an Assistant Professor at the Georgia Institute of Technology’s Nuclear & Radiological Engineering and Medical Physics Programs, leading the Radiological Engineering, Detection, and Dosimetry (RED²) Laboratory. She holds affiliations within the George W. Woodruff School of Mechanical Engineering. Her research focuses on computational dosimetry, radiation detection, and nuclear policy, emphasizing applications in emergency response, nuclear safety, and nonproliferation. Education: Ph.D. (2014), M.S. (2009), and B.S. (2006) in Nuclear and Radiological Engineering/Physics from Georgia Tech, UBC, and UC Berkeley. Prior roles include faculty positions at Texas A&M University and research at Oak Ridge National Laboratory. Research interests span advanced radiation transport modeling, AI-driven dosimetry, nuclear safeguards, and policy analysis. Key projects include developing pediatric dosimetry models, radiation detector validation, and nuclear emergency preparedness. Awards include the Elda E. Anderson Award (2018) and recognition from the National Academies (2020-2023). Recent articles highlight innovations in computational phantoms, radiation triage systems, and nonproliferation policy analysis. The RED² Lab engages in interdisciplinary work with U.S. military and health sectors, emphasizing real-world applications of radiation science. Grants & Collaborations: Partnerships with ORNL, DoD, and AllREMS consortium for radiological defense systems. Labs & Teams: The RED² Lab hosts graduate students focusing on dosimetry, detection, and policy. Notable projects include accelerator shielding design and nuclear forensics.

• Pulmonary Hypertension
• Cardiac Imaging
• MRI Techniques
• +۳ مورد دیگر

Professor Andy Swift is a Professor of Cardiothoracic Radiology at the University of Sheffield's School of Medicine and Population Health. He specializes in clinical applications of novel imaging techniques, particularly MRI, for pulmonary and cardiac diseases. His research focuses on leveraging machine learning for disease assessment and prognosis in pulmonary hypertension. Dr. Swift has held roles including NIHR Cardiovascular Clinical Fellow and Wellcome Trust Clinical Career Development Fellow. Education: MBChB from University of Sheffield (2003), followed by radiology training in Hull. He completed a PhD on MRI in Pulmonary Hypertension via NIHR Biomedical Research Fellowship. Research Interests: Machine learning in medicine, MRI as a diagnostic/prognostic tool (e.g., DITCH, INSITGATE projects), chronic thromboembolic pulmonary hypertension, and imaging biomarkers for genetic variants (COHORT study). Current projects include AI-driven lung parenchymal analysis and CMR-derived hemodynamic metrics. Key Contributions: Over 150 peer-reviewed articles on pulmonary hypertension imaging, including seminal work on CMR prognostic models and CT-derived risk stratification. Collaborates internationally on trials like REPAIR, evaluating therapies and imaging endpoints in PAH. Facilities: Based at University MRI Unit, Royal Hallamshire Hospital. Engages in multi-disciplinary teams for translational research, bridging radiology, cardiology, and computational science.

• Medical Image Analysis
• MRI Brain Imaging
• Deep Learning
• +۷ مورد دیگر

Mark Jenkinson - Professor of Neuro-Imaging Mark Jenkinson is a Professor of Neuro-Imaging at the University of Adelaide, affiliated with the School of Computer and Mathematical Sciences and the Australian Institute for Machine Learning. His primary focus lies in advancing medical image analysis methodologies, particularly in MRI brain imaging. He specializes in developing deep learning, statistical, and model-based techniques for tasks such as anatomical/lesion segmentation, big data analysis, normative modeling, and motion correction. Research Interests: Medical image segmentation and registration Deep learning applications in neuroimaging MRI artifact reduction and harmonization Clinical fMRI for language mapping Biomarker-driven disease progression analysis (e.g., Parkinson’s, multiple sclerosis) Key Contributions: Development of tools like BIANCA-MS for automated lesion segmentation Pioneering work on diffeomorphic networks for brain template creation Advances in multi-site MRI harmonisation frameworks (e.g., FedHarmony) Affiliations: Faculty of Sciences, Engineering and Technology Australian Institute for Machine Learning Grants/Supervision: Eligible to supervise Masters and PhD students in computational neuroscience and medical AI. Active in collaborative projects involving international imaging consortia. Labs/Teams: Leads interdisciplinary teams integrating computer science and clinical neuroimaging for translational research.