Igor Vidic

Igor Vidic has a wealth of experience in the field of medical imaging and machine learning. Igor began their career in 2012 as a Research Assistant at the Institute of Physics Belgrade, where they analyzed data obtained at the ATLAS detector in CERN, programming in C/C++, high-performance computing (PROOF), python, and Linux administration. Igor also contributed to the upgrade of the Liquid Argon Calorimeter at the ATLAS detector in CERN by doing simulation and validation of new sensors cells. In 2014, they started a PhD at NTNU, where they developed a new, potentially clinical, diffusion MR sequence for breast cancer imaging and established an image preprocessing pipeline (Matlab) and developed analysis software (Matlab). Igor also conducted analysis and machine learning for data and advising collaborators (SPSS and Python (numpy, scikit-learn, tensorflow)). In 2018, they worked as a Machine Learning Engineer at Elliptic Labs, where they contributed to the development of a machine learning based proximity sensor for mobile phones using ultrasound and managed proximity and gesture/approach projects and performed statistical analysis. Igor also formulated new models for time series classification (keras, tensorflow, scikit-learn). That same year, they also worked as a Computer Vision Engineer at OptoScale, where they played a key role in a project optimizing fish weight calculation and introducing machine learning into the company's pipeline as it developed a stereo vision camera for estimating the fish weight. Igor leveraged optimization of calculation to increase precision of the fish weight calculation by 150g and reduced standard deviation of the measurement by 30% through the development of the deep neural network for classification of the fish boundaries. In 2020, they started working as an Imaging Scientist at Cortechs.ai, where they charted successful courses for the translation of research into product, including the development of new (DW) MRI and AI models and the creation of two FDA 510k cleared products while collaborating with three team members. Igor also spearheaded the development of a glioma segmentation deep learning model (topping competition scores for pre-op cases and introducing post-op) and developed a model for prostate MRI segmentation (CNN, pytorch) and preprocess pipelines of the medical imaging data (python).

Igor Vidic began their education in 1998 at the Mathematical Grammar School, where they studied Mathematics. From 2002 to 2011, they attended the Faculty of Electrical Engineering at the University of Belgrade, earning a Bachelor's degree in Biomedical/Medical Engineering. Igor then went on to pursue a Master's Degree in Computer Science at the Universidad Politécnica de Madrid from 2011 to 2012. Finally, Igor Vidic completed their Doctor of Philosophy - PhD in Physics at the Norwegian University of Science and Technology (NTNU) from 2014 to 2019.