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53822-01 - Lecture: Deep Learning for Medical Image Analysis (3 CP)
| Semester | spring semester 2026 |
| Course frequency | Every spring sem. |
| Lecturers |
Florentin Bieder (florentin.bieder@unibas.ch)
Philippe Claude Cattin (philippe.cattin@unibas.ch, Assessor) |
| Content | This course provides an introduction to deep learning and how this cutting-edge technology can be applied to medical image analysis with exercises using PyTorch. The course covers the following topics * gradient based optimization * backpropagation * multilayer perceptrons (MLPs) * convolutions and convolutional Neural Networks (CNNs) * network building blocks (activations, pooling, normalization, etc.) * losses and metrics for regression and classification problems * parameter initialization heuristics * data preprocessing and encoding * data augmentation * resource consumption * common architectures (transformers, generative adversarial models, diffusion models, etc.) |
| 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 |
| 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
| Modules |
Module Specialisation: Medical Nanosciences (Master's Studies: Nanosciences) Module: Biomedical Engineering Electives (Master's Studies: Biomedical Engineering) Module: Electives in Data Science (Master's Studies: Data Science) |
| 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) |