جست‌وجوی استاد و پژوهشگر

• Oncology
• Geriatric Surgery
• Surgical Outcomes
• +۵ مورد دیگر

Prof. dr. Barbara van Leeuwen is a full professor at the University of Groningen's Faculty of Medical Sciences, where she leads research in surgical oncology and geriatric care. Her work focuses on optimizing outcomes for elderly cancer patients through innovative surgical approaches and patient-centered care models. Her research spans: Oncologic surgery outcomes and risk stratification Geriatric oncology and frailty assessment Shared decision-making in cancer treatment Arts in health interventions (music therapy) Health economics in surgical oncology Recent publications demonstrate strong focus on melanoma management, sarcoma diagnostics, and perioperative care optimization, with emerging work on non-pharmacological interventions like music therapy for surgical patients. Scientific Recognition: National representative for the International Society of Geriatric Oncology (SIOG) since 2020 She leads two research groups: 'Innovative Clinical Studies and Long-term Treatment Consequences in Cancer' (InClinS) and 'Cure and Care in the Community Context' (FOUR-C), supervising multiple research projects on surgical outcomes and patient-centered care.

• Liver Transplantation
• Surgery
• Hepatobiliary Surgery
• +۳ مورد دیگر

Marieke de Boer is a researcher affiliated with the University of Groningen's Faculty of Medical Sciences and the Groningen Institute for Organ Transplantation (GIOT). Her work focuses on hepatobiliary surgery, liver transplantation, and surgical oncology, particularly in cholangiocarcinoma and post-transplant outcomes. She has contributed to 99 research outputs, including studies on resectability classifications, surgical outcomes, and molecular signatures in transplant recipients. She actively participates in international collaborations and has organized events such as the European-African Hepato-Pancreato-Biliary Association Congress and the Alpine Liver and Pancreatic Surgery Meeting. Her research addresses topics like failure-to-rescue rates in cholangiocarcinoma surgery, cellular mechanisms in intestinal grafts, and quality-of-life metrics in kidney transplant patients. She co-authored national consensus guidelines on resectability criteria for perihilar cholangiocarcinoma. Media highlights include pioneering liver transplantations for colorectal cancer patients with liver metastases and contributions to pandemic-related material aid for Suriname.

• Sports Medicine
• Epidemiology
• Injury Prevention
• +۳ مورد دیگر

Dr. John Orchard is an Adjunct Professor at the Sydney School of Public Health, University of Sydney, and serves as the Chief Medical Officer for Cricket Australia and Cricket NSW. He has over 20 years of experience in sports and exercise medicine, with expertise in sports injury epidemiology, tendinopathy, and cardiac health in athletes. Dr. Orchard holds an MBBS from the University of Melbourne (1989), an MD by thesis (2006), and a PhD in sports injury epidemiology from UNSW (1999). He has published over 300 scientific papers and maintains an H-index of 70. His research focuses on sports injury prevention, musculoskeletal disorders, and the epidemiology of cricket and football injuries. He has pioneered the Orchard Sports Injury and Illness Classification System (OSIICS), a global standard for injury reporting in sports. Dr. Orchard’s work extends to elite athlete cardiac screening and the management of ground surface-related injuries. Education: MBBS, University of Melbourne (1989) PhD in Sports Injury Epidemiology, UNSW (1999) MD by thesis, University of Melbourne (2006) Awards: Member of the Order of Australia (AM, 2020) for contributions to sports medicine and cricket. Advising & Grants: Teaches registrars in the Australasian College of Sport and Exercise Physicians program. Conducts research funded by Cricket Australia, the Australian Football League, and international sports organizations. Labs/Teams: Chief Medical Officer roles with Cricket Australia, Cricket NSW, and the Sydney Southeast Asia Centre. Leads the Australasian Registry of Screening ECGs in National Athletes (ARENA) initiative.

Bashir Dodoمشاهده پروفایل

مدرس

• Medical Image Analysis
• Algorithm Development
• Software analysis and design
• +۳ مورد دیگر

Bashir Dodo is a Lecturer (Teaching and Scholarship) in the Department of Computer Science at the University of York, where he has served since 2021. Previously, he held roles at Umaru Musa Yaradua University, Katsina (Lecturer II from 2013–2021) and London Brunel International College (Lecturer 2018–2019). His qualifications include a BSc in Software Engineering (University of East London, 2011), MSc in Computer Systems Engineering (2013), and PhD in Computer Science from Brunel University London (2020). His research focuses on Medical Image Analysis, Algorithm Development, and Software analysis/design. Notable contributions include advancements in retinal OCT image segmentation and skin cancer detection using sub-region features. He has published extensively on medical imaging techniques and computer vision applications in healthcare. Dr. Dodo's work bridges computer science and biomedical engineering, with applications in diagnostics and healthcare technology. His recent publications (2017–2020) emphasize automated medical image analysis methods for retinal layers and skin lesions.

Ayişe Karadağمشاهده پروفایل

استاد

• Chronic Wound Management
• Tissue Integrity Issues
• Person-Centered Care
• +۵ مورد دیگر

Prof. Ayişe Karadağ is a Professor at Koç University School of Nursing, where she has served since 2015. Previously, she held academic roles at Gazi University (2000–2015), including Head of the Department of Nursing (2011–2015) and Dean of the School of Nursing (2017–2024). She graduated from Hacettepe University with a Bachelor’s (1992), Master’s (1994), and PhD (1999) in Nursing. Her career includes research assistantship at Hacettepe University (1993–2000) and visiting scholar positions at institutions like California State University, San Bernardino (2004), Kent State University (2007), and the University of Massachusetts (2024). Her research focuses on chronic wound management, tissue integrity, person-centered care, and nursing education. She pioneered the Certified Stoma and Wound Care Nursing Education Programs in Turkey, now led at Koç University’s Semahat Arsel Nursing Education and Research Center since 2014. She has authored over 100 peer-reviewed articles and books, addressing topics like pressure injuries, incontinence-associated dermatitis, and ostomy care. Educations: Bachelor of Nursing, Hacettepe University (1992) Master of Nursing, Hacettepe University (1994) PhD in Nursing, Hacettepe University (1999) Her awards include the 2024 Fellow of the American Academy of Nursing and the 2022 ISUCRS Oral Presentation Third Prize. She has contributed to academic leadership roles, including Vice Director of Gazi University’s School of Nursing (2008) and Associate Head (2009–2011). Her work emphasizes evidence-based practices, telehealth adaptation during the pandemic, and innovative educational methods like serious games for patient training.

Lasse Riis Østergaardمشاهده پروفایل

دانشیار

• Medical Imaging
• Biomedical Engineering
• Image Processing and Computer Vision
• +۴ مورد دیگر

Lasse Riis Østergaard is an Associate Professor at the Department of Health Science and Technology, Aalborg University, affiliated with The Faculty of Medicine. His research focuses on medical imaging, AI applications in healthcare, and computational methods for analyzing medical images, particularly in CT and MRI. He leads projects such as 'Modelling and quantification of the cerebral cortex' and contributes to initiatives like the Danish Centre for Health Informatics and AI for the People. Key research areas include: Medical Image Analysis: Specializing in segmentation, artifact reduction, and quantitative measurements in CT/MRI AI in Surgery: Developing deep learning models for surgical action recognition and robotic skill assessment Clinical Applications: Investigating body composition via CT, lung function correlations, and placental vascular imaging He has authored over 120 publications and supervised 5 PhD students. Notable collaborations include work with the Virtual Brain project and NemoMed. Recent studies explore ethnic differences in body composition and AI-driven surgical training. Awards: None explicitly listed, but his work has received media attention including coverage in Surgical Endoscopy and Advances in Respiratory Medicine .

• Pathology
• Digital Pathology
• Transplant Pathology
• +۶ مورد دیگر

Sudhir Perincheri, MD, PhD, is an Assistant Professor of Pathology at Yale School of Medicine and serves as Director of Digital Pathology. He completed his residency in Anatomic Pathology/Clinical Pathology as well as fellowship training in hematopathology, renal and genitourinary pathology in the Departments of Pathology and Laboratory Medicine at Yale. Dr. Perincheri's research focuses on the application of emerging tools in anatomic pathology, with particular expertise in digital pathology. His work bridges clinical pathology with technological innovations, especially in the transition from traditional glass slides to digital imaging systems that may eventually incorporate artificial intelligence algorithms for more accurate disease diagnosis. His research portfolio shows a strong emphasis on renal pathology, transplant pathology, and genitourinary tumors. Analysis of his recent publications reveals a consistent focus on glomerular diseases, mechanisms of transplant rejection, and diagnostic innovations in hematopathology. His work often involves collaborative research with other Yale faculty in applying advanced imaging techniques like MultiPhoton Microscopy to renal and prostate pathology. Scientific Recognition Postdoctoral Fellowship Award from Juvenile Diabetes Research Foundation (2006) Dr. Perincheri maintains active clinical responsibilities, regularly diagnosing complex cases involving blood cancers, kidney disorders, and genitourinary malignancies. His role as Director of Digital Pathology places him at the forefront of technological transformation in the field, working on the development and implementation of digital imaging systems that could revolutionize how pathology is practiced. He serves on several professional committees including the ECOG-ACRIN Multiple Myeloma Committee, ECOG-ACRIN Laboratory Science and Pathology Committee, Medical Record and Clinical Information Committee, and the College of American Pathology Digital and Computational Pathology Committee. These roles demonstrate his leadership in advancing pathology practice standards and digital transformation in the field.

• Cytopathology
• Gynecological Pathology
• Diagnostic Biomarkers
• +۳ مورد دیگر

Minhua Wang, MD, PhD, is an Assistant Professor of Pathology at Yale School of Medicine with dual clinical expertise in cytopathology and gynecological pathology. She specializes in microscopic analysis of fluid, tissue, and cytology specimens to guide cancer diagnosis and management. Her work emphasizes improving diagnostic accuracy through biomarker research and standardized reporting systems, particularly for pancreaticobiliary lesions and gynecological malignancies. Education: Cytopathology Fellowship: M.D. Anderson Cancer Center (2019) Surgical Pathology Fellowship: M.D. Anderson Cancer Center (2018) Residency: University at Buffalo (2017) PhD: University of Houston College of Optometry (2008) Research Focus: Biomarker validation for cancer diagnostics, prognostic cytopathology applications, and implementation of standardized diagnostic frameworks. Her studies aim to enhance clinical decision-making through precise pathological evaluation. Publications: Recent work includes validation of ACR-TI-RADS systems, prognostic analyses of endometrial cancer, and comparative immunomarker studies in serous fluids. Her research bridges cytological techniques with molecular and clinical outcomes. Professional Contributions: Board-certified in both anatomic/clinical pathology and cytopathology. Active in advancing diagnostic standards through WHO and international reporting systems.

Liliana Caldeiraمشاهده پروفایل

پژوهشگر

• Artificial Intelligence in Medicine
• Medical Image Analysis
• Radiology
• +۴ مورد دیگر

Dr. Liliana Caldeira serves as a Core Scientist and team leader at the Center for Data and Simulation Science (CDS) within the University Hospital of Cologne, University of Cologne. She directs innovative data science research in the Department of Diagnostic and Interventional Radiology, specializing in AI algorithm development for 3D medical imaging analysis including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Research Focus: Her work centers on clinical AI implementation in radiology, with expertise in deep learning for medical image segmentation (brain tumors, lymph nodes, organs), image denoising, and disease classification. Key applications include brain metastasis detection in melanoma, lymph node staging for cancer, kidney segmentation in polycystic disease, and pulmonary hypertension assessment—emphasizing clinical validation and multicenter study designs to bridge AI research with practical medical workflows. Publication Trends: Analysis of her 2020-2023 publications reveals consistent focus on clinically validated AI tools, particularly using 3D convolutional neural networks for segmentation tasks. Her research demonstrates strong collaboration across radiology subspecialties, with shared last authorship indicating principal investigator leadership in translating algorithms to clinical practice. Scientific Awards: No awards were documented in the provided text. Advising and Grants: Student mentorship and grant funding details were not specified in the source material. Labs and Teams: Dr. Caldeira leads a dedicated data science team within the radiology department, collaborating with CDS under Scientific Domain 2 (Computational Biology and Medicine). The team focuses on high-quality dataset creation, AI evaluation frameworks, and algorithm implementation for medical imaging diagnostics, operating from the University Hospital campus at Kerpener Str. 62.

Arron Laceyمشاهده پروفایل

مدرس ارشد

• Health Data Science
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Dr Arron Lacey is a Senior Lecturer in Health Data Science and Natural Language Processing at Swansea University Medical School. He has been based at the SAIL Databank since 2011. His academic background includes a BSc in Physics, MSc in Computer Science, and a PhD in Healthcare Studies from Swansea University. Education: BSc Physics, Swansea University MSc Computer Science, Swansea University PhD Healthcare Studies, Swansea University Research Interests: Focuses on applying health data science and natural language processing (NLP) to healthcare challenges. Key areas include clinical data extraction from unstructured records, cardiovascular epidemiology, epilepsy outcomes analysis, and genomic data integration. His work emphasizes population-level data linkage and translational research. Recent Research Trends: Recent publications highlight NLP applications in extracting structured data from clinical letters, cardiovascular disease management post-intervention, and epilepsy mortality during the pandemic. His work bridges computational methods with real-world healthcare data to improve diagnosis, treatment monitoring, and public health policy. Grants & Collaborations: Led grants including HDR UK-funded NLP implementation projects (2017). Collaborates with multidisciplinary teams on initiatives like MedGATE for scalable NLP healthcare frameworks. Labs/Teams: Core member of the SAIL Databank, part of Swansea University's health data science ecosystem. Involved in the Center for Doctoral Training in Computation.

Joshua L. Warrenمشاهده پروفایل

دانشیار

• Spatial Statistics
• Spatiotemporal Modeling
• Bayesian Statistics
• +۷ مورد دیگر

Joshua L. Warren is an Associate Professor in the Department of Biostatistics at the Yale School of Public Health. His academic appointments include primary affiliation with Biostatistics and secondary affiliations with Climate Change and Health, Public Health Modeling, Yale Superfund Research Center, and Yale-BI Biomedical Data Science Fellowship. Dr. Warren received his Ph.D. in Statistics from North Carolina State University in 2011 and completed his postdoctoral training at the University of North Carolina at Chapel Hill. He has been a faculty member at Yale University since July 2014. Dr. Warren's research focuses on statistical methods in public health with an emphasis on environmental health problems. His work primarily involves: Introducing spatial and spatiotemporal models in Bayesian frameworks Studying associations between environmental exposures (particularly air pollution) and health outcomes Developing methods for analyzing data related to preterm birth, low birth weight, and congenital anomalies Applying statistical models in collaborative settings including epidemiology, geography, nutrition, and glaucoma research Theoretical work in spatial/spatiotemporal modeling and Bayesian nonparametrics His recent publications demonstrate expertise across diverse areas including environmental health, infectious disease modeling (particularly tuberculosis), reproductive health, and glaucoma research. His work frequently employs sophisticated spatial statistical methods to analyze complex public health data and identify critical windows of exposure to environmental hazards. Scientific Awards Coauthor, Kenneth Rothman Epidemiology Prize Paper (2018) First Author, Best Paper in Biometrics (2012) Dr. Warren serves as an Associate Editor for Statistics in Biosciences (2015-present) and previously for the Journal of the American Statistical Association - Theory and Methods (2018-2020). He has also served as a reviewer for organizations including the Health Effects Institute, Swiss National Science Foundation, and NIH. His research collaborations include frequent work with Nicole Deziel, Daniel Weinberger, Ted Cohen, Xiaomei Ma, Virginia Pitzer, and Yu Lan.

• Translational Hematology
• Cancer Immunology
• Somatic Mutations
• +۶ مورد دیگر

Dr. Satu Mustjoki is a Professor of Translational Hematology at the Faculty of Medicine, University of Helsinki (UH), Finland. She serves as Director of the Translational Immunology Research Program (2019-2025), Scientific Director of the iCAN Digital Precision Cancer Medicine Flagship (2022-2026), and leads multiple international research consortia including EraPerMed EU and Heal-Art projects. Adjunct Professor (2011), Experimental Hematology, UH Specialist in Clinical Chemistry (2008), UH PhD (2001), UH MD (1997), UH Her research focuses on somatic mutations in hematological malignancies and autoimmune diseases, cancer immunology, and personalized medicine. The work integrates immunogenomics, functional genomics, and clinical translation to identify novel therapy targets and biomarkers. Recent publications highlight somatic mutation analysis in T cells (chronic GVHD, RA), NK cell interactions with blood cancers, and single-cell approaches to melanoma immunotherapy. Key themes include mutation-driven immune dysregulation and precision targeting of apoptotic pathways in leukemia. Helsinki Institute of Life Science (HiLife) Fellow (2017-2025) ERC Consolidator grant awardee (2015-2020) Biomedicum Helsinki award (2018) City of Helsinki Science award (2020) Academy of Finland Senior Research Fellowship (2010-2015) She supervises 9 completed + 9 ongoing post-doctoral researchers, 18 completed + 13 ongoing PhD students, and 22 completed MSc/MD theses. Funding includes major grants from the Erkko Foundation, Leukemia & Lymphoma Society, and Academy of Finland. She participates in European and American hematology scientific committees and leads international clinical studies as principal investigator.

Markku Hauta-Kasariمشاهده پروفایل

استاد

• Spectral Imaging
• Pattern Recognition
• Computer Vision
• +۳ مورد دیگر

Markku Hauta-Kasari is the 1st Vice Dean and a Professor of Computer Science at the University of Eastern Finland (UEF), affiliated with the School of Computing under the Faculty of Science, Forestry and Technology. He holds a MSc (1994) and PhD (1999) in computer science and information processing from the University of Kuopio and Lappeenranta University of Technology, respectively. His career includes roles as Director of InFotonics Center Joensuu (2003–2010) and Head of the School of Computing (2014–2021). As a researcher, he focuses on spectral color science, pattern recognition, and computer vision with applications in biomedical imaging, material analysis, and clinical diagnostics. His work bridges academia and industry through projects like the BioColour Library and the ODSI-DB dental imaging database. Key research areas include hyperspectral imaging for tumor detection, spectral analysis of biological tissues, and optimizing light sources for medical visualization. He has led over 50 peer-reviewed publications and contributed to international collaborations, including visiting research roles in Japan. He chairs the Pattern Recognition Society of Finland and the CIE TC8-07 Technical Committee on Multispectral Imaging. Hauta-Kasari’s innovations span clinical tools like real-time spectral imaging systems for neurosurgery and dental diagnostics, leveraging machine learning for improved diagnostic accuracy. His laboratories emphasize interdisciplinary approaches to solve challenges in medicine, forestry, and material science.

• Medical Imaging
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Dr Abdullah Nazib is a Research Fellow at Queensland University of Technology (QUT) in the Faculty of Engineering, School of Electrical Engineering & Robotics. His research focuses on applying advanced computational techniques to medical imaging challenges, particularly in cancer detection and treatment planning. His research interests include: Medical image registration and segmentation Deep learning architectures for healthcare applications 3D medical image analysis Prostate cancer detection and grading Organ segmentation for radiation therapy Radiomics-based diagnostic systems Analysis of Dr Nazib's publication history reveals a clear evolution from general computer vision research toward specialized medical applications. His recent work demonstrates significant contributions to uncertainty quantification in medical image segmentation and multimodal learning approaches for diagnostic imaging. The 2024 publications particularly highlight his focus on clinically relevant applications with immediate potential impact on cancer diagnosis and treatment planning. His collaborative research network includes strong partnerships with Dr Fookes, Dr Perrin, and other members of QUT's biomedical imaging group, reflecting an interdisciplinary approach that bridges computer science, engineering, and clinical medicine to develop practical AI solutions for healthcare challenges.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Tolga Tasdizen is a Professor in the Department of Electrical & Computer Engineering and an Adjunct Professor at the School of Computing, University of Utah. His research bridges computer vision, machine learning, and interdisciplinary applications in medical imaging, nuclear forensics, and urban health. Primary affiliation: Department of Electrical & Computer Engineering, University of Utah Secondary affiliation: School of Computing, University of Utah Research Interests Developing novel deep learning frameworks for histopathological image analysis and medical signal processing Applying computer vision to characterize built environments and examine public health outcomes Advancing nuclear forensic techniques through material morphology and machine learning Exploring biases in AI models for clinical and epidemiological applications Creating explainable AI workflows for medical diagnostics Analyzing urban infrastructure impacts on traffic safety and chronic disease prevalence Article Trends demonstrate expertise in: Medical imaging (histopathology, ECG analysis, chest X-rays) with applications in Alzheimer's disease and cancer diagnostics Urban health studies using Google Street View data to assess built environments' impacts on obesity, diabetes, and traffic injuries Nuclear forensics through SEM image analysis of uranium oxides and actinide materials Robust AI training techniques (contrastive learning, domain adaptation) for clinical and environmental datasets Collaborative Networks span radiology, cardiology, epidemiology, and nuclear engineering disciplines. His work often involves multi-institutional teams and emphasizes scalable data collection methods like eye-tracking and computer vision.