معرفی
Anders Eklund is a Senior Associate Professor at Linköping University, affiliated with both the Department of Biomedical Engineering (IMT) and the Department of Computer and Information Science (IDA). He holds roles in the Center for Medical Image Science and Visualization (CMIV), focusing on developing advanced methods for medical image analysis, including applications in pediatric brain tumor classification, synthetic image generation, and federated learning frameworks. His research bridges biomedical engineering, artificial intelligence, and data science to address clinical challenges such as improving diagnostic accuracy and reducing healthcare resource bottlenecks.
Education: Master of Science (2007), PhD in Medical Informatics (2012), Postdoc at Virginia Tech (2012–2014), Docent (2016), Associate Professor (2021).
Research Interests: Medical image processing, deep learning for healthcare, federated learning for sensitive data, synthetic medical image generation, and statistical validation of neuroimaging techniques. Notable contributions include exposing flaws in fMRI analysis software and advancing AI-driven solutions for radiation treatment planning.
Publications: Recent work emphasizes deep learning applications in medical imaging, including tumor classification, federated learning for privacy-preserving models, and synthetic image techniques to address data scarcity. His articles reflect a focus on interdisciplinary solutions for healthcare challenges, such as reducing queues post-pandemic through automated brain image analysis.
Grants & Advising: Secured a 3.6M SEK grant from the Swedish Research Council (2016). Supervised numerous PhD students, including main supervision of Xuan Gu (2019) and co-supervision of Per Sidén (2020). Active in training the next generation of biomedical engineers and AI researchers.
Labs & Collaborations: Leads projects within CMIV and collaborates across departments at Linköping University. Involved in initiatives like the ASSIST project, which aims to automate brain image analysis to free clinician time.
