Language of Instruction |
Türkçe |
Course Type |
Elective Courses |
Course Instructor(s) |
DOKTOR ÖĞRETİM ÜYESİ SEMA CANDEMİR |
Mode of Delivery |
in class |
Prerequisites |
Knowledge in linear algebra, probability and statistics. Knowledge in computer programming. Python language will be used in class project. Knowledge in machine learning and image processing. |
Courses Recomended |
The recommended courses are image processing and machine learning. |
Recommended Reading List |
1) Deep Learning - Aaron Courville, Ian Goodfellow, and Yoshua Bengio https://www.deeplearningbook.org/ 2) Convolutional neural networks: an overview and application in radiology - PubMed (nih.gov)3) O’Reilly - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems4) Magician’s Corner: 9. Performance Metrics for Machine Learning Models | Radiology: Artificial Intelligence (rsna.org)5) S. H. Park, K. Han, Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction, Radiology 286 (3) (2018) 800–809. 6) Deep Learning (Nature 2015) - Seminal review paper by LeCun, Bengio, and Hinton https://www.researchgate.net/publication/277411157_Deep_Learning 7) Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide | Radiology (rsna.org) |
Assessment methods and criteria |
Participation 5% Assignments 35% Course Project/Presentation 60% |
Work Placement |
Course Project - Students will describe a healthcare problem, propose a medical data analysis solution in image processing and machine learning disciplines, formulate and develop a method to solve the problem. |