IOANNIS (JOHN) D. APOSTOLOPOULOS
Foundation for Research and Technology Hellas (FORTH)
Institute of Chemical Engineering Sciences (ICE-HT)
Center of Studies on Air quality and Climate Change (C-STACC)
University of Thessaly, Department of Energy Systems
Artificial Intelligence, Computational Methods and Technological Applications (ACTA) Lab
University of Patras, School of Medicine, Department of Medical Physics
FIELDS OF STUDY AND IMPLEMENTATION
APPLICATIONS OF DEEP LEARNING AND MACHINE LEARNING IN MEDICAL IMAGING
EXPLAINABLE ARTIFICIAL INTELLIGENCE
AIR QUALITY MONITORING DEVICES
AIR QUALITY MONITORING LOW-COST SENSOR CALIBRATION USING ARTIFICIAL INTELLIGENCE
APPLICATIONS OF DEEP LEARNING IN INDUSTRY
DEEP LEARNING FOR ENVIRONMENTAL MONITORING APPLICATIONS
DEEP LEARNING IN PRECISION AGRICULTURE
IMAGING MODALITIES
Specialized in the following
COMPUTED TOMOGRAPHY
CT uses X-rays to create detailed cross-sectional images of the body, which can help diagnose various medical conditions such as fractures, tumors, and infections.
POSITRON EMISSION TOMOGRAPHY
PET involves injecting a small amount of radioactive material into the body, which emits positrons that can be detected by a special camera. This technique can be used to identify abnormal metabolic activity in organs and tissues, which can help diagnose cancer, heart disease, and other conditions.
SINGLE-PHOTON EMISSION COMPUTED TOMOGRAPHY
SPECT uses a similar principle to PET, but uses a different type of radioactive material that emits gamma rays. SPECT is often used to study blood flow and brain activity.
MAGNETIC RESONANCE IMAGING
MRI (Magnetic Resonance Imaging) uses strong magnetic fields and radio waves to produce detailed images of the body. Unlike CT and PET, which use ionizing radiation, MRI is non-invasive and does not involve exposure to ionizing radiation. MRI is often used to visualize soft tissues and organs, such as the brain, spine, and joints.
ARTIFICIAL INTELLIGENCE METHODS
MACHINE LEARNING AND DEEP LEARNING METHODS
MACHINE LEARNING MODELS
Decision Trees, Logistic Regression, Neural Networks, Support Vector Machines, Ensemble methods
FUNDAMENTAL DEEP LEARNING MODELS
Convolutional Neural Networks, Recurrent Neural Networks, LSTMs
ADVANCED DEEP LEARNING
Generative Adversarial Networks, Attention Modules, Transformers
