Information Package / Course Catalogue
| Course Code | : TG011 |
| Course Type | : Area Elective |
| Couse Group | : Short Cycle (Associate's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 2 |
| Prt. | : 0 |
| Credit | : 2 |
| Lab | : 0 |
| ECTS | : 3 |
This course aims to teach students the basics of artificial intelligence in radiology and show how this technology is applied in medical imaging processes.
This course introduces students to the basic concepts of artificial intelligence and machine learning techniques in radiology. It addresses how artificial intelligence is applied in the field of medical imaging with examples and shows students how to process and analyze medical images using these technologies. It also addresses the ethical and legal dimensions of artificial intelligence applications, aiming to make students aware and responsible in this field.
| Lec. Halit KIZILET |
| 1. | Students will understand the concepts of artificial intelligence and machine learning and grasp the basic applications of these technologies in the field of radiology. |
| 2. | They will learn imaging techniques and image processing, classification and analysis methods using artificial intelligence. |
| 3. | They will learn how various medical imaging techniques solve real patient and imaging problems. |
| 4. | They will understand the current developments in the field of artificial intelligence and radiology. |
| 5. | They will understand the ethical and legal limits of artificial intelligence applications in the field of medical imaging. |
| 1. | Deep Learning for Medical Image Analysis, 2023, S. Kevin Zhou, Hayit Greenspan, Dinggang Shen |
| 2. | Machine and Deep Learning in Oncology, Medical Physics and Radiology, 2022, Issam El Naqa, Martin J. Murphy |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %40 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 2 | 2 | 56 |
| Midterm Examination | 1 | 8 | 1 | 9 |
| Final Examination | 1 | 9 | 1 | 10 |
| TOTAL WORKLOAD (hours) | 75 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | PÇ-13 | PÇ-14 | PÇ-15 | PÇ-16 | PÇ-17 | PÇ-18 | PÇ-19 | PÇ-20 | |
OÇ-1 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||||||||
OÇ-2 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||||||||
OÇ-3 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||||||||
OÇ-4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||||||||
OÇ-5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | |||||||