At International Congress SPIE Medical Imaging celebrated on San Diego (California, USA) on February 16-21, has distinguished itself with the Robert F. Wagner Award All-Conference Best Student Paper Award (Runner Up) to the work presented by the researcher of the ULPGC, Himar Fabelo Gómez, entitled "Surgical Aid Visualization System for Glioblastoma Tumor Identification based on Deep Learning and In-Vivo Hyperspectral Images of Human Patients".
This work is the result of a close collaboration between the bioengineering team of the University of Texas at Dallas (USA) and the DSI division of the Institute of Applied Microelectronics (IUMA) of the ULPGC and collects the results of applying 'Deep Learning' to the identification of high-grade brain tumors through the use of hyperspectral images.
The study is part of the ITHaCA project that develops the Institute of Applied Microelectronics of the ULPGC, a multidisciplinary project that integrates engineers, neurosurgeons and pathologists. Its main objective is to perform a precise differentiation and classification by using hyperspectral images of the different types of brain tumors. This differentiation / classification will be done in real time using advanced machine learning or machine learning algorithms that will be accelerated using high performance hardware platforms. It will be based on supervised classification algorithms to identify the type of tumor and unsupervised classification to detect its edges.
SPIE Medical Imaging is one of the largest medical imaging congresses in the world, hosting 1,200 researchers from around the world this year. The Robert F. Wagner Prize is one of the most prestigious internationally and recognizes the best articles of the entire conference.
The leading researchers in image processing, physics, computer-aided diagnosis, perception, image-guided procedures, biomedical applications, ultrasound, computer science, radiology and digital pathology present the latest information. It also focuses on fast emerging areas such as deep learning, artificial intelligence and machine learning.
The subject to be discussed focuses on the latest innovations related to underlying fundamental scientific principles, technological developments, scientific evaluation and clinical applications. Thus the main lines of study in this edition have revolved around: Physics of medical imaging, Image processing, Computer-assisted diagnosis, Image-guided procedures, robotic interventions and modeling, Perception of the image, Observer performance and evaluation of technology, Biomedical applications in molecular, structural and functional images, Computer imaging for health services, research and applications, Tomography and ultrasound tomography and digital pathology.