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.
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.
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.
Göksu TURAÇ graduated from the Computer Engineering Department and successfully completed the Artificial Intelligence and Machine Learning Minor Program at Eskişehir Technical University. Her research interests include machine learning and computer vision. As part of her thesis project, she and her teammate developed explainable AI-based models that detect eye diseases from fundus images. This work was awarded first place at the 17th Project Fair and Competition of Eskişehir Technical University and was presented at the Engineering Sciences and Research Student Congress held at Ankara Atılım University.
Mahmut Sami YILMAZ is a student in the Computer Engineering Department at Eskişehir Technical University. He is passionate about software development, cloud computing, and deep learning. His research interests are deep learning and explainable AI with a particular focus on semi-supervised training methods. His current project is developing a deep learning model for the detection of gastrointestinal diseases from medical images.
Ahmet Emirhan TERZİ is a computer engineering student at Eskişehir Technical University. He has a strong interest in cybersecurity, software engineering, and deep learning. His research interests include deep learning, explainable AI with a focus on semi-supervised training methods. Currently, he is working on designing a deep learning-based system to identify gastrointestinal disorders from medical images.
Seda YELER is a computer engineering student at Eskişehir Technical University. She is interested in backend development, cloud computing, and artificial intelligence. As part of her graduation project, she and her teammate are developing and explainable AI-based model to detect gastrointestinal diseases from medical images.