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Image for learning opportunity Introduction to AI-based biomedical image analysis for cell segmentation
micro-module

Introduction to AI-based biomedical image analysis for cell segmentation

Introduction to AI-based biomedical image analysis for cell segmentation

Exploring cell segmentation with modern image analysis techniques
Open for application
DescriptionInformationValue and progressProviders

Description

In this micro-module, you will explore how AI and deep learning methods can be used to segment and analyse cells in microscopy images. Combining theoretical foundations with hands-on computational exercises, you will learn to apply image analysis pipelines, evaluate model performance, and extract meaningful biological information from complex data. Working in a flexible online environment, you will develop practical skills relevant for research and applications in biomedical science and medical diagnostics.

The Team

Teachers

Meet the expert teaching staff, who is here to guide you through this learning opportunity.

MB
Magnus Borga
Teacher
PH
Pierre Hakizimana
Teacher

Learners

0 learners
Study period
21 September – 16 October 2026
Study format
Online
Application period
5 June – 6 September 2026
Credits
2.5 ECTS
Hosting university
Linkoping University
Learner type
Students
EQF Level

Master Level

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Information

Value and progress

AI methodologies in microscopy images

After completing the course, the student is expected to be able to describe AI-based methodologies for the segmentation of cells in microscopy images.

ESCO SKILLS

Deep learning models

After completing the course, the student is expected to be able to use pre-trained deep learning models for the segmentation of cells in microscopy images.

ESCO SKILLS

Evaluation of models

After completing the course, the student is expected to be able to evaluate the quality of a segmentation model using established statistical metrics.

ESCO SKILLS

Principles of deep learning

After completing the course, the student is expected to be able to explain the principles of deep learning and how neural networks are used in biomedical image analysis.

ESCO SKILLS

Quantitative measure extraction

After completing the course, the student is expected to be able to extract quantitative measures of cell size, shape, and spatial organization from segmented images.

ESCO SKILLS

Image analysis pipelines

After completing the course, the student is expected to be able to implement reproducible image analysis pipelines using interactive computational environments.

ESCO SKILLS

Assessment of AI image analysis

After completing the course, the student is expected to be able to discuss the opportunities and limitations of AI-based image analysis in biomedical and clinical applications.

ESCO SKILLS

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Hosting university

Linkoping University

Linkoping University