Dr. Sema CANDEMIR: is an Assistant Professor in the Computer Engineering Dept. at Eskişehir Technical University. Her areas of expertise include medical image analysis, image processing, computer vision and machine learning, with a focus on region-of-interest detection, deep convolutional neural networks, algorithmic solutions for limited medical data analysis, and integration of imaging modalities with non-imaging clinical data. She worked as a postdoctoral researcher and research scientist at the U.S. National Library of Medicine (NLM), National Institutes of Health between 2013 and 2018. Subsequently, she worked as a research scientist in the Dept. of Radiology, Artificial Intelligence Laboratory, at the Ohio State University Wexner Medical Center. In 2022, Dr. Candemir received the Tubitak 2232-A International Leading Researchers Grant. In 2017 and 2016, she received Team Honorary Award from the NLM. In 2017, she received the Best Paper Award in the 30th IEEE Int. Sypm. on Computer-Based Medical Systems. She and her team were also recognized with the U.S. Dept. of Health and Human Services Ignited Pathway Award for Automatic X-ray screening for Rural Areas in 2014. Dr. Candemir serves on the Editorial Boards of Radiomics and Artificial Intelligence, Frontiers in Nuclear Medicine and Turkish Journal of Electrical Engineering and Computer Sciences. She has also been an active reviewer for medical image analysis journals, including IEEE Trans. on Medical Imaging, Journal of Digital Imaging and IEEE Journal of Biomedical and Health Informatics.
Umut Kaan KAVAKLI: is a recent graduate of Eskişehir Technical University, Computer Engineering Department. He served as a research assistant in the AI in Healthcare LAB and undergraduate teaching assistant in the AI in Healthcare course during 2023-2024 academic year. His research interests are deep learning, image processing and explainability AI. His current focus is semi-supervised training and loss functions. His research contributions are "Addressing Class Imbalance in Covid-19 Lesion Segmentation" and "Covid-19 Lesion Segmentation with Self-Training". His graduation thesis "Detection of Defective 2D Materials with Self-training Method" is supported by TUBITAK 2209-A program.
Salih KIZILIŞIK: is a dedicated computer engineering student at Eskişehir Technical University. His current focuses are exploring the fundamental principles of XAI models and how explainability can enhance weakly supervised approaches in medical image analysis.
Kaan TOPÇU, MSc.: is a graduate of Information Systems from Middle East Technical University, where he completed his master`s degree. His master`s research focused on applying convolutional neural networks to predict Covid-19 risks from CT images. Kaan`s research interests include computer vision, image processing and machine learning. Since earning his bachelor`s degree in Industrial Engineering from Middle East Technical University in 2018, he has been working as a data analyst at various tech companies, where he has led and taken part in data preprocessing, machine learning model development, and data analysis projects.
Soner ALTUN: is a research assistant in the Department of Computer Engineering at Eskişehir Technical University, where he is also pursuing a master`s degree in Artificial Intelligence Program. Before joining Eskişehir Technical University, he gained industry experience in intelligence systems. His research focuses on artificial intelligence, particulary in machine learning, deep learning and image processing, with an interests in their applications in healthcare. His thesis research is dedicated to the detection of lung diseases by identifying affected regions in medical images using state-of-the-art artificial intelligence techniques.