Title of Talk: Medical Artificial Intelligence, Algorithms and Applications

Short Biography:
Prof.Dr. Tülay Yıldırım received his bachelor's degree in 1990 and master's degree in 1992 from Yıldız Technical University (YTU), Department of Electronics and Communication Engineering, and in 1997 he received his PhD from Liverpool University, Department of Electrical and Electronics Engineering. After working as an R&D engineer in a private company for a short period, she started her academic career as a research assistant in Yıldız Technical University (YTU), Department of Electronics and Communication Engineering in 1991. She became Assistant Professor in 1997, Associate Professor in 2000 and Professor in 2006 and held various administrative positions such as Head of Department and Vice Dean. Her scientific studies have focused on artificial intelligence, intelligent systems, biomedical instrumentation, biometric person recognition/identification systems, cyber-physical systems and electronic circuit design. Prof.Dr. Tülay Yıldırım who has been involved in various projects as a manager, researcher or consultant, has executive over 40 doctoral and master's thesis and has more than 200 international and national publications. She has taken part in the organizing, advisory and evaluation committees of many scientific activities in her field and since 2004, she has been the president of the ASYU-INISTA symposium chain on intelligent systems. She is the director of Cyber ​​Security and Biometric Research Consultancy and Testing Center within Yıldız Technical University and the founder of MEDALab-Machine Intelligence and Electronic Design Automation Laboratory.

Title of Talk: The Use of Artificial Intelligence as a New Diagnosis Method in Diseases whose Diagnosis Method is not Defined

Title of Talk: Introduction to Deep Learning for Physicians
Artificial Intelligence Applications on Radiology Report Texts

Title of Talk: Dr. Artificial intelligence

Short Biography:
Graduated from Bornova Anadolu Lisesi, 1978. MD diploma from Aegean University Medical Faculty, 1984. Chairman of Radiology and Medical Informatics Departments. 2001-2011; 2005-2018. Vice Dean of DEU School of Medicine, 2000-2006. Dean of DEU School of Medicine, 2016-2018. Main interest in radiology: breast, interventional and abdominal radiology. Interested with medical informatics since 2005. Present President of Turkish Medical Informatics Association. One international and 3 national books; About 80 SCI indexed scientific articles. Health Services Manager Award in 2010. Several roles in European Society of Radiology; Education Com., European Board of Radiology, ETAP etc. Highly involved in medical education and training. President of Turkish Medical Education Association, 2008-2010. Hobbies: Caricature, collecting humor journals, music and wood works

Title of Talk: Structuring of Artificial Intelligence Units in Hospitals: Chicago Experience

Title of Talk: Explainable Artificial Intelligence

Title of Talk: Classification of Mammography Images Using Deep Learning

Abstract:
Mammography is the main method for breast cancer screening and it contributes to the reduction of breast cancer mortality rate through early detection. The importance of early detection of breast cancer has led computer scientists to develop systems which would help radiologists who work under heavy workload in improving their workflow and in identifying the patients that need medical prioritization. In this context, convolutional neural network (CNN) model, which is a deep learning technique used successfully in medical image processing field, has also been intensively used to classify mammography images using the BIRADS system. This speech provides an overview of the state of the art in classifying mammography images according to BIRADS categories using deep learning techniques. Finally, a recent project which is being carried out in corporation with Ege University Faculty of Engineering Computer Engineering Department and Ege University Faculty of Medicine Radiology Department in this area will be introduced.

Title of Talk: Drug Drug Interaction (DDI) Prediction with Machine Learning

Abstract:
Drug-drug interactions (DDIs), which occasionally arise through co-prescription of a drug with other drug(s), may cause an undesired effect other than its principal pharmacological action. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Thus, there is clear need for automated methods for predicting DDIs. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this talk, an overview of these DDI prediction methods will be given and a work which aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings will be represented.

Short Biography:
Asst.Prof.Dr. Özgür Gümüş, received his bachelor's degree in 1997, master's degree in 2001 and doctorate degree in 2008 from Ege University Department of Computer Engineering. He worked at Ege University Computer Engineering Department as a research assistant between 1997-2006 and as a lecturer between 2006-2009. He is still Works as an assistant professor in the same department. Current research topics include the application of data science and machine learning methods to health and life science problems and bioinformatics.

Title of Talk: Artificial Intelligence Technologies in Interpreting Brain CT Images

Abstract:
Requirements involving automated classification of medical images possess challenges that exceed those that arise in processing natural images. In spite of the apparent success of the conventional machine-learning approaches using convolutional neural networks that gave rise to recognition of complicated shapes, medical images contain holistic features like area, volume and symmetry as well as shape. Although deep learning technology is often trusted for overcoming these difficulties, in many cases a multi-technique approach is a better choice.

Short Biography:
Asst.Prof.Dr. Ahmet Egesoy has graduated from Middle East Technical University, Department of Computer Engineering in 1994 and acquired his MSc degree From Ege University Computer engineering Department in 2000. In 2010 he received his PhD degree from the same school. He has served the Computer Engineering Department as a Research Assistant after 2002 and as an instructor since 2012. He has worked on Automated Theorem Proving, Expert Systems and Fuzzy Logic. He is currently the instructor of Knowledge Representation course and is interested in Model-Driven Software Engineering as well as languages and semotics.

Title of Talk: Heart Beat Classification Using Artificial Intelligence

Abstract:
Differentiating patient's normal and abnormal heart beats by using AI methods and detecting emergency situations as early as possible are aimed. Early identification will help to deliver immediate/early treatment for those at risk before the cardiac event develops. It is a multidisciplinary system where engineering technology meets medical science.

Title of Talk: Applications of Artificial Intelligence on Health in the UK

Abstract:
In recent years, with the advances in technology, artificial intelligence has become quite popular and it has started to provide solutions to problems of every aspect of daily life. This presentation will provide some insight into the problems in the UK health domain and the solutions provided to these problems using artificial intelligence.