Advancing In-Vivo Human Brain Tissue Classification: A Multimodal Approach Integrating Machine Learning, Hyperspectral Imaging and Depth Information
Abstract
This talk will present a novel approach to classify in vivo human brain tissue in real time using a multimodal imaging technique that integrates hyperspectral imaging and depth information in a recently developed system called Slim Brain. This system has been used during surgical procedures to collect a database of over 193 patients that is growing weekly. By applying sophisticated machine learning algorithms to the acquired information, Slim Brain demonstrates the potential of combining advanced imaging modalities with computational techniques for precise and non-invasive assessment of brain tissue, with implications for neurology and medical imaging. In addition, a new acquisition system under development that combines hyperspectral imaging with spatial frequency domain imaging modulation will be presented as a new tool to improve brain tissue classification.
Biography
Alberto Martín-Pérez is a Teaching Assistant at the Department of Audivisual Engineering and Communication in the School of Telecommunications Systems and Engineering of the Technical University of Madrid (UPM). Presently, he is pursuing a PhD at UPM with the Electronic and Microelectronic Design Group (GDEM) in the Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM). His research pursuits center around the utilization of Machine Learning algorithms for the classification of in-vivo human brain tumors through hyperspectral imaging. Furthermore, he aims to enhance classification methodologies through the application of spatial frequency domain imaging for his doctoral studies.
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