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Course Detail

Course Description

CourseCodeSemesterT+P (Hour)CreditECTS
MEDICAL IMAGE ANALYSIS-Spring Semester3+036
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of CourseEnglish
Course LevelFirst Cycle (Bachelor's Degree)
Course TypeElective
Course CoordinatorAssist.Prof. Cihan Bilge KAYASANDIK
Name of Lecturer(s)Assist.Prof. Cihan Bilge KAYASANDIK
Assistant(s)
AimThe 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.
Course ContentThis course contains; Course Introduction
,Medical Data Acquisition,
,Introduction to Computer vision and signal processing
,Data Preprocessing
,Convolution and special filters
, Image Segmentation with Conventional Methods
,Student Paper presentations
,Machine Learning basics
,Machine Learning methods on image analysis
,Validation methods for small data analysis
,Artificial neural networks/ Autoencoders I
,Artificial neural networks/ Autoencoders II
,Deep learning applications for medical data
,Deep learning applications for medical data II
.
Dersin Öğrenme KazanımlarıTeaching MethodsAssessment Methods
1. Ability to identify problems and deficiencies in medical data analysis and create solution routes for existing problems10, 14, 16, 18, 19, 2, 3, 9A, D, F
2. Ability to identify and apply filters and filtering methods appropriate to the features to be extracted from an image. 10, 14, 16, 18, 19, 2, 3, 9A, D, F
3. Being able to use a mathematical perspective and put it into practice in order to obtain the necessary information in medical data analysis. 10, 14, 16, 18, 19, 2, 3, 9A, D, F
4. Ability to use deep learning methods from machine learning and artificial neural networks in medical data analysis 10, 14, 16, 18, 19, 2, 3, 9A, D, F
Teaching Methods:10: Discussion Method, 14: Self Study Method, 16: Question - Answer Technique, 18: Micro Teaching Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 3: Problem Baded Learning Model, 9: Lecture Method
Assessment Methods:A: Traditional Written Exam, D: Oral Exam, F: Project Task

Course Outline

OrderSubjectsPreliminary Work
1Course Introduction
Lecture slides, reading assigned papers
2Medical Data Acquisition,
Lecture slides, reading assigned papers
3Introduction to Computer vision and signal processing
Lecture slides, reading assigned papers
4Data Preprocessing
Lecture slides, reading assigned papers
5Convolution and special filters
Lecture slides, reading assigned papers
6 Image Segmentation with Conventional Methods
Lecture slides, reading assigned papers
7Student Paper presentations
Lecture slides, reading assigned papers
8Machine Learning basics
Lecture slides, reading assigned papers
9Machine Learning methods on image analysis
Lecture slides, reading assigned papers
10Validation methods for small data analysis
Lecture slides, reading assigned papers
11Artificial neural networks/ Autoencoders I
Lecture slides, reading assigned papers
12Artificial neural networks/ Autoencoders II
Lecture slides, reading assigned papers
13Deep learning applications for medical data
Lecture slides, reading assigned papers
14Deep learning applications for medical data II
Lecture slides, reading assigned papers
Resources
1. Fundamentals of Medical Imaging, Suetens, P., Cambridge University Press, 2. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, Yoo, Terry S., CRC Pressö 3. Duda, R. O., & Hart, P. E. (2006). Pattern classification. John Wiley & Sons.

Course Contribution to Program Qualifications

Course Contribution to Program Qualifications
NoProgram QualificationContribution Level
12345
1
An ability to apply knowledge of mathematics, science, and engineering
2
An ability to identify, formulate, and solve engineering problems
X
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
7
An ability to communicate effectively
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context

Assessment Methods

Contribution LevelAbsolute Evaluation
Rate of Midterm Exam to Success 30
Rate of Final Exam to Success 70
Total 100
ECTS / Workload Table
ActivitiesNumber ofDuration(Hour)Total Workload(Hour)
Course Hours14342
Guided Problem Solving000
Resolution of Homework Problems and Submission as a Report6212
Term Project428
Presentation of Project / Seminar2612
Quiz000
Midterm Exam8648
General Exam8864
Performance Task, Maintenance Plan000
Total Workload(Hour)186
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(186/30)6
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.

Detail Informations of the Course

Course Description

CourseCodeSemesterT+P (Hour)CreditECTS
MEDICAL IMAGE ANALYSIS-Spring Semester3+036
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of CourseEnglish
Course LevelFirst Cycle (Bachelor's Degree)
Course TypeElective
Course CoordinatorAssist.Prof. Cihan Bilge KAYASANDIK
Name of Lecturer(s)Assist.Prof. Cihan Bilge KAYASANDIK
Assistant(s)
AimThe 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.
Course ContentThis course contains; Course Introduction
,Medical Data Acquisition,
,Introduction to Computer vision and signal processing
,Data Preprocessing
,Convolution and special filters
, Image Segmentation with Conventional Methods
,Student Paper presentations
,Machine Learning basics
,Machine Learning methods on image analysis
,Validation methods for small data analysis
,Artificial neural networks/ Autoencoders I
,Artificial neural networks/ Autoencoders II
,Deep learning applications for medical data
,Deep learning applications for medical data II
.
Dersin Öğrenme KazanımlarıTeaching MethodsAssessment Methods
1. Ability to identify problems and deficiencies in medical data analysis and create solution routes for existing problems10, 14, 16, 18, 19, 2, 3, 9A, D, F
2. Ability to identify and apply filters and filtering methods appropriate to the features to be extracted from an image. 10, 14, 16, 18, 19, 2, 3, 9A, D, F
3. Being able to use a mathematical perspective and put it into practice in order to obtain the necessary information in medical data analysis. 10, 14, 16, 18, 19, 2, 3, 9A, D, F
4. Ability to use deep learning methods from machine learning and artificial neural networks in medical data analysis 10, 14, 16, 18, 19, 2, 3, 9A, D, F
Teaching Methods:10: Discussion Method, 14: Self Study Method, 16: Question - Answer Technique, 18: Micro Teaching Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 3: Problem Baded Learning Model, 9: Lecture Method
Assessment Methods:A: Traditional Written Exam, D: Oral Exam, F: Project Task

Course Outline

OrderSubjectsPreliminary Work
1Course Introduction
Lecture slides, reading assigned papers
2Medical Data Acquisition,
Lecture slides, reading assigned papers
3Introduction to Computer vision and signal processing
Lecture slides, reading assigned papers
4Data Preprocessing
Lecture slides, reading assigned papers
5Convolution and special filters
Lecture slides, reading assigned papers
6 Image Segmentation with Conventional Methods
Lecture slides, reading assigned papers
7Student Paper presentations
Lecture slides, reading assigned papers
8Machine Learning basics
Lecture slides, reading assigned papers
9Machine Learning methods on image analysis
Lecture slides, reading assigned papers
10Validation methods for small data analysis
Lecture slides, reading assigned papers
11Artificial neural networks/ Autoencoders I
Lecture slides, reading assigned papers
12Artificial neural networks/ Autoencoders II
Lecture slides, reading assigned papers
13Deep learning applications for medical data
Lecture slides, reading assigned papers
14Deep learning applications for medical data II
Lecture slides, reading assigned papers
Resources
1. Fundamentals of Medical Imaging, Suetens, P., Cambridge University Press, 2. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, Yoo, Terry S., CRC Pressö 3. Duda, R. O., & Hart, P. E. (2006). Pattern classification. John Wiley & Sons.

Course Contribution to Program Qualifications

Course Contribution to Program Qualifications
NoProgram QualificationContribution Level
12345
1
An ability to apply knowledge of mathematics, science, and engineering
2
An ability to identify, formulate, and solve engineering problems
X
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
7
An ability to communicate effectively
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context

Assessment Methods

Contribution LevelAbsolute Evaluation
Rate of Midterm Exam to Success 30
Rate of Final Exam to Success 70
Total 100

Numerical Data

Student Success

Ekleme Tarihi: 09/10/2023 - 10:37Son Güncelleme Tarihi: 09/10/2023 - 10:37