Deep learning and computer vision in health
| Title | Βαθιά μάθηση και όραση υπολογιστών στην υγεία / Deep learning and computer vision in health |
| Code | OD12 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Panagiotis Bamidis |
| Common | No |
| Status | Active |
| Course ID | 600024793 |
Programme of Study: Joint PPS "Managing Digital Transformation in the Health Sector" (2024-today)
Registered students: 37
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Data Science | EPILEGOMENA EIDIKEUSĪS | 2 | 1 | 6 |
| Academic Year | 2024 – 2025 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600256608
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600256608
- eLearning (Moodle): https://elearning.digihealthedu.eu/course/view.php?id=15
Language of Instruction
- English (Instruction, Examination)
Learning Outcomes
LO1: To represent an image in different color spaces and in the frequency domain
LO2: To perform typical image processing operations
LO3: To extract low-level characteristics from an image
LO5: To implement an automatic learning system based on classic algorithms for image content classification
LO5: To know the typical architecture of a convolutional neural network (CNN) and to understand how it works
LO6: To solve a medium complexity image classification problem using CNNs
LO7: To apply transfer learning / fine-tuning methodologies based on pre-trained CNNs
LO8: To use deep learning algorithms for image objects identification
LO9: To know deep learning algorithms for automatic generation of multimedia content
LO10: To manipulate images using the OpenCV library and use the Tensorflow library to develop automatic learning applications
LO11: Healthcare applications
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Respect natural environment
- Advance free, creative and causative thinking
Course Content (Syllabus)
PC1 Image representation
PC2 Image operations
PC3 Extraction of image features
PC4 Introduction to machine learning
PC5 Artificial neural networks
PC6 Convolutional neural networks
PC7 Transfer Learning
PC8 Network architectures for detecting and identifying image objects
PC9 Network architectures for automatic content generation
PC10 Developed Health Care Applications
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- Podcast
- Multimedia
- Interactive excersises
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.6 | ✓ | ||
| Reading Assigment | 21 | 0.8 | ✓ | ||
| Project | 20 | 0.8 | ✓ | ||
| Written assigments | 70 | 2.8 | ✓ | ||
| Total | 150 | 6 |
Student Assessment
Student Assessment methods
- Written Assignment (Formative, Summative)
- Report (Formative, Summative)
Bibliography
Additional bibliography for study
Feature Extraction and Image Processing for Computer Vision, 4th Edition, M. Nixon e Alberto Aguado, Academic Press, 2019
Deep Learning, I. Goodsfellow, Y. Bengio e A. Courville, MIT Press, 2016
Learning OpenCV 4 with Python 3, 3rd Edition, Joseph Howse, Joe Minichino, Packt Publishing, 2020
Tutoriais e documentação das bibliotecas OpenCV e Tensorflow
Last Update
12-07-2024
