MEDICAL DATA ANALYSIS

AIM

The course aims to show how to identify the problems in medical sciences and how to approach them. We will introduce mathematical techniques to extract information from medical images. We will show how to analyze medical images according to different purposes and help diagnose diseases. Medical image analysis is a highly interdisciplinary field involving medicine, computer science, mathematics, biology, statistics, probability, psychology, and other fields. The course includes topics in medical image acquisitions: basics of Xray CT, Ultrasound, MRI and fMRI; image preprocessing: image denoising, image filtering, and basic filter design, image enhancement, feature extraction; image segmentation: local and adaptive thresholding, active contour and level set methods, edge detection, basic texture analysis; image registration, tracking; machine learning and deep learning for the feature extraction and segmentation purposes in medical images. This course will be application-oriented. Assignments will be based on a literature review, paper presentation, and computer implementations.

CONTENT

This course contains; ,,,,,,,,,,,,,.

1. 1

   1. identify problems in the medical image analysis and possible solution frameworks for the determined problems

   Taught by: Discussion Method, Problem Solving Method, Case Study Method, Question - Answer Technique, Micro Teaching Technique, Brainstorming Technique, Project Based Learning Model, Simulation Technique, Problem Baded Learning Model, Computer-Internet Supported Instruction, Inquiry-Based Learning, Experiential Learning, Lecture Method · Assessed by: Oral Exam, Project Task

2. 2

   2. Determine the correct filter for extracting certain features from the images and algorithm to apply them

   Taught by: Discussion Method, Problem Solving Method, Case Study Method, Question - Answer Technique, Micro Teaching Technique, Brainstorming Technique, Simulation Technique, Problem Baded Learning Model, Computer-Internet Supported Instruction, Inquiry-Based Learning, Experiential Learning, Lecture Method · Assessed by: Oral Exam, Project Task

3. 3

   3. use of mathematical tools to design tools for certain purposes of medical data analysis

   Taught by: Discussion Method, Case Study Method, Self Study Method, Question - Answer Technique, Micro Teaching Technique, Brainstorming Technique, Simulation Technique, Problem Baded Learning Model, Computer-Internet Supported Instruction, Inquiry-Based Learning, Experiential Learning, Lecture Method · Assessed by: Oral Exam, Project Task

4. 4

   4. use machine learning and deep learning methods for classification of medical data.

   Taught by: Discussion Method, Problem Solving Method, Case Study Method, Question - Answer Technique, Micro Teaching Technique, Brainstorming Technique, Project Based Learning Model, Simulation Technique, Problem Baded Learning Model, Computer-Internet Supported Instruction, Lecture Method · Assessed by: Oral Exam, Project Task

WEEKLY PLAN

1. WEEK 1

2. WEEK 2

3. WEEK 3

4. WEEK 4

5. WEEK 5

6. WEEK 6

7. WEEK 7

8. WEEK 8

9. WEEK 9

10. WEEK 10

11. WEEK 11

12. WEEK 12

13. WEEK 13

14. WEEK 14

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

READING

• 1. Fundamentals of Medical Textbook Imaging, Suetens, P., Cambridge University Press, 2. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, Yoo, Terry S., CRC Press

TEACHING STAFF

• Assist.Prof. Cihan Bilge GÜRBÜZCOORDINATOR
• Assist.Prof. Cihan Bilge GÜRBÜZ