Deep Learning in Medical Image Analysis for Personalized Medicine
— Prof. Irena Galić Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Croatia
Abstract
Medical image analysis is an important field that involves the analysis and interpretation of medical images to aid its diagnosis, treatment, and research. Recent advances in machine and deep learning methods have significantly improved the accuracy and efficiency of medical image analysis. Machine and deep learning methods have been ... successfully applied in several applications of medical image analysis, such as image classification, segmentation, and pathologies detection. However, the field still faces several challenges and limitations, including limited availability of annotated data, variability in medical images, and the need for interpretability. There are several opportunities for future research, including developing explainable deep learning methods, integrating multi-modal data, adopting standardized evaluation metrics, and advancing personalized medicine. Collaboration between medical professionals, computer scientists, and researchers from various disciplines is crucial to address these challenges and realize the full potential of medical image analysis.
This speech will provide insight into the latest advances in medical image analysis, including machine and deep learning methods to improve classification, segmentation, and pathologies detection, limited availability of annotated data, and the need for interpretability. The speech will give future research directions in medical image analysis and the importance of collaboration between medical professionals, computer scientists, and researchers from various disciplines to address the challenges and realize the full potential of medical image analysis in personalized medicine.
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Biography
Irena Galić is a Full Professor at the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Croatia where she heads the Department of Software Engineering and Research Group for Computer Science and Human-Computer Interaction.
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Irena Galić was born in Osijek, Croatia. She received the Diploma degree in mathematics and computer science from J. J. Strossmayer University of Osijek, Croatia, the M.Sc. degree in computer science from Saarland University, Saarbrücken, Germany,, and the Ph.D. degree from J. J. Strossmayer University of Osijek, in 2011. Her research at Saarland University was supported by Max Planck Research School for Computer Science. She was a Vice-Dean for Science and Postgraduate Studies at Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, where she introduced and teaches courses in the field of signals and systems and visual computing at undergraduate, graduate and postgraduate levels. She is a member of the Croatian Academy of Engineering and was a technical program co-chair for IEEE conferences Smart Systems and Technologies (2018-2020) and a general co-chair for IEEE conference IWSSIP 2019, Osijek, Croatia. She is a member of editorial boards of scientific journal International Journal of Electrical and Computer Engineering Systems. Irena Galić has actively participated in several Croatian research projects and was lead project investigator of Croatian Science Foundation’s Installation Research Project “Medical Image Interpretation Methods for a Detailed Heart Health Analysis”. She is an author and a co-author of over 80 published scientific papers and her major research contribution is in lossy image compression with partial differential equations and lately in medical image analysis.
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