Ioannis Gkouzionis was born and raised in the city of Thessaloniki, Greece. He received his Diploma of Engineering in Electrical and Computer Engineering from the Technical University of Crete in 2017. After his graduation, Ioannis started studying towards his M.Sc. degree in Electronic and Computer Engineering at TUC, until August 2019. His research focused on Machine Learning methods for the detection and classification of skin lesions in Hyperspectral Imaging. In 2019, Ioannis conducted research at IMEC in Leuven, Belgium in the context of Erasmus internship. There he joined NERF and specifically the FarrowLab led by Prof. Karl Farrow. His research work in that lab focused on the application of Deep Learning methods for the automated segmentation of neurons from confocal images.
Ioannis is currently a PhD Researcher at the Hamlyn Centre for Robotic Surgery and the Department of Surgery and Cancer at Imperial College London. His work focuses on Machine Learning, Deep Learning and Medical Imaging for image-guided interventions and cancer diagnosis and treatment, under the supervision of Prof. Daniel Elson and Dr. Chris Peters. Ioannis aims at solving multidisciplinary problems especially those related to the application of Machine and Deep Learning methods on Biomedical Hyperspectral Imaging.
- Research interests: Biophotonics, Medical Image Processing, Machine and Deep Learning
- Specialities: Biomedical Imaging, Multi/Hyperspectral Imaging, Optoelectronics, Machine Learning
- Fluent in Python and MATLAB; experienced with C/C , Java, and a variety of other languages
- Experienced with SQL, Git, and UNIX environment
et al., 2022, Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancers using diffuse reflectance spectroscopy for resection margin assessment, Jama Surgery, ISSN:2168-6254
et al., 2022, Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery, Journal of Biomedical Optics, Vol:27, ISSN:1083-3668
et al., 2022, Self-supervised depth estimation in laparoscopic image using 3D geometric consistency, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), SPRINGER INTERNATIONAL PUBLISHING AG, Pages:13-22, ISSN:0302-9743
et al., Discriminating between cancer and healthy tissue in upper gastrointestinal cancer surgery using deep learning and diffuse reflectance spectroscopy, London Surgery Symposium
et al., Real-time tissue classification in stomach and oesophageal cancer based on optical tracking of a diffuse reflectance spectroscopy probe, Photonics West: Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX