About the webinar
This webinar will present Artificial Intelligence for CT medical imaging, focusing on automated methods for large-scale body composition analysis. The presentation will introduce deep learning techniques for image segmentation, image registration and deep regression methodes of CT images, enabling detailed assessment of tissues such as muscle, adipose tissue, and organs.
Participants will gain insights how AI can be used to automatically analyze CT scans for body composition, enabling research in metabolic diseases and population studies, and explore challenges and future directions in applying AI to large-scale body composition analysis.
Who is the webinar for?
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Researchers working in medical imaging, AI, or data science
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PhD students and academics interested in AI for healthcare
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Data scientists and machine learning engineers working with medical data
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Clinicians and radiologists interested in AI-assisted image analysis
Key takeaways for participants:
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How AI and deep learning enable automated analysis of CT medical images
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Methods for large-scale body composition analysis
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How medical imaging data can be used to study metabolic and health-related conditions
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Challenges and future directions in AI-driven medical imaging research
Speaker bio:
Nouman Ahmad holds a Ph.D. in Medical Science (Data Science) from Uppsala University, Sweden. His research focuses on developing artificial intelligence methods for medical image segmentation, registration, and quantitative analysis of CT imaging data. His work centers on analyzing large-scale medical imaging datasets to better understand body composition and metabolic diseases.
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