Michal Brzus
SPIE Involvement:
Author
Area of Expertise:
Artificial Intelligence , Neuroimaging , Medical Imaging , Software Engineering , Machine Learning , MRI
Websites:
Profile Summary



Michal Brzus is a Ph.D. Candidate in Electrical and Computer Engineering at the University of Iowa committed to advancing the field of AI in healthcare. Starting at Lodz University of Technology in Poland, he studied Robotics Engineering before receiving an athletic scholarship, joining the University of Iowa as a Division I swimmer, and graduating with a Computer Science Bachelor's degree in 2019. His subsequent career as a Software Developer ignited a passion for Artificial Intelligence (AI), propelling him towards higher education.


Michal's research centers on AI-based applications in medical imaging. He emphasizes developing techniques that effectively bridge the gap between research and clinical settings. His work is grounded in software engineering best practices to create high-quality, reliable software that translates innovative research into practical, real-world solutions. His commitment to impactful innovation underscores his dedication to pioneering AI-driven solutions that stand to transform healthcare.
Publications (2)

Proceedings Article | 2 April 2024 Presentation + Paper
Michal Brzus, Cavan Riley, Joel Bruss, Aaron Boes, Randall Jones, Hans Johnson
Proceedings Volume 12931, 1293108 (2024) https://doi.org/10.1117/12.3006568
KEYWORDS: Data modeling, Magnetic resonance imaging, Image classification, Education and training, Image processing, Medical imaging, Feature extraction, Decision trees, Biomedical applications, Performance modeling, Classification systems, Machine learning, Random forests

Proceedings Article | 4 April 2022 Poster + Presentation + Paper
Michal Brzus, Alexander Powers, Kevin Knoernschild, Jessica Sieren, Hans Johnson
Proceedings Volume 12032, 1203229 (2022) https://doi.org/10.1117/12.2611008
KEYWORDS: Brain, Neuroimaging, Head, Image processing, 3D modeling, Image segmentation, Image resolution, Medical imaging, Magnetic resonance imaging, Machine learning

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