16Jan2024
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16Jan2024
Seminar: “Overview of Deep Learning Projects in Medical Imaging”
Abstract: Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering and especially in medical image analysis. In this talk, Julia will present her past and recent projects on deep learning based medical image classification, segmentation, image synthesis and image reconstruction in a variety of image acquisition domains such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Optical Coherence Tomography (OCT), X-Ray and Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM).
Speaker: Julia Dietlmeier
Senior Postdoctoral Researcher
Insight SFI Research Centre for Data Analytics at Dublin City University, Dublin, Ireland
Short-Bio: Julia Dietlmeier received her Dipl.-Ing.(FH) degree in Electrical Engineering with the specialization in Wireless Communication from Munich University of Applied Sciences, Munich, Germany.
She completed her Master of Science degree in Electrical and Computer Engineering from Portland State University, Portland, Oregon, USA where she was also working as a Teaching and a Research Assistant.
Julia completed her PhD degree in Electronic Engineering with the specialization in Computer Vision and Machine Learning from Dublin City University, Dublin, Ireland in 2017. Her PhD thesis “A Machine Learning Approach to the Unsupervised Segmentation of Mitochondria in Subcellular Electron Microscopy Data” investigated applications of unsupervised machine learning techniques to the biomedical segmentation problems in electron microscopy domains.
Currently Julia is a senior postdoctoral researcher within the Insight SFI Research Centre for Data Analytics at DCU, Ireland. Her research interests involve deep learning, electron microscopy, pattern analysis, statistical communication theory, information-theoretical problems and applied numerical methods. She is a member of IEEE and European Society for Molecular Imaging (ESMI).
Location and date: IEETA auditorium, 16th January 2024, 15h00
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