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53822-01 - Lecture: Advanced Methods in Medical Image Analysis (3 CP)

Semester spring semester 2024
Course frequency Every spring sem.
Lecturers Florentin Bieder (florentin.bieder@unibas.ch)
Philippe Claude Cattin (philippe.cattin@unibas.ch, Assessor)
Julia Wolleb (julia.wolleb@unibas.ch)
Content This course provides an introduction to deep learning and how this cutting-edge technology can be applied to medical image analysis. The course covers the following topics
• Fundamentals of deep learning
• Numerical optimization (for training machine learning models)
• Multilayer perceptrons
• Convolutional Neural Networks (CNNs) and their medical applications
• Segmentation with CNNs
• Autoencoders
• Generative models
• Deep learning models for sequential data

Learning objectives • Understand the basics of deep learning and how it can be applied to medical image analysis
• Understand numerical optimization algorithms used to train deep learning models
• Understand the architecture and training of multilayer perceptrons and CNNs
• Medical applications of MLPs and CNNs for classification, regression, segmentation, and anomaly detection tasks
• Know different generative models and their medical applications
• Know appropriate models for sequential data analysis
Weblink DBE MA BME

 

Admission requirements (C15) Medical Imaging and Medical Image Processing; Python Knowledge similar to course 69472
Limited student numbers, priority given to student in Biomedical Engineering
Language of instruction English
Use of digital media No specific media used

 

Interval Weekday Time Room
wöchentlich Wednesday 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097

Dates

Date Time Room
Wednesday 28.02.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 06.03.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 13.03.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 20.03.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 27.03.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 03.04.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 10.04.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 17.04.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 24.04.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 01.05.2024 15.15-17.00 Tag der Arbeit
Wednesday 08.05.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 15.05.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 22.05.2024 15.15-17.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Wednesday 29.05.2024 15.15-17.00 Hegenheimermattweg 167B, Teaching Laboratory 02.098
Wednesday 26.06.2024 09.00-11.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Tuesday 28.01.2025 14.00-16.00 Hegenheimermattweg 167B, Lecture Hall 02. 097
Modules Modul: Biomedical Engineering Electives (Master's Studies: Biomedical Engineering)
Module Specialisation: Medical Nanosciences (Master's Studies: Nanosciences)
Module: Electives in Data Science (Master's Studies: Data Science)
Module: Image-Guided Therapy (Master's Studies: Biomedical Engineering (Start of studies before 01.08.2023))
Assessment format record of achievement
Assessment details written exam
Assessment registration/deregistration Reg.: course registration, dereg: cancel course registration
Repeat examination one repetition, best attempt counts
Scale 1-6 0,1
Repeated registration as often as necessary
Responsible faculty Faculty of Medicine
Offered by Departement Biomedical Engineering (DBE)

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