Teaching

A small, hands-on bootcamp course to familiarize life science researchers with state-of-the-art deep learning techniques for microscopy image analysis and introduce tools and frameworks that enable independent application of the material.

The goal of this course is to familiarize researchers in the life sciences with state-of-the-art deep learning techniques for microscopy image analysis and to introduce them to tools and frameworks that facilitate independent application of the learned material after the course.

The advent of deep learning has brought a revolution in the field of computer vision, including most tasks and research questions concerned with microscopy image analysis. Neural networks have been successfully used for image restoration, classification and segmentation, for the detection of objects and characterisation of their morphology, and for high-throughput imaging and large-scale processing in 3D. Despite these advances, training and deployment of such neural networks remains difficult for practitioners of image analysis. The aim of our course is to close this gap and teach the participants, in the most hands-on way possible, to apply deep learning-based methods to their own data and image analysis problems.

The advent of deep learning has brought a revolution in the field of computer vision, including most tasks and research questions concerned with microscopy image analysis. Neural networks have been successfully used for image restoration, classification and segmentation, for the detection of objects and characterisation of their morphology, and for high-throughput imaging and large-scale processing in 3D. Despite these advances, training and deployment of such neural networks remains difficult for practitioners of image analysis. The aim of our course is to close this gap and teach the participants, in the most hands-on way possible, to apply deep learning-based methods to their own data and image analysis problems.

The goal of this course is to familiarize researchers in the life sciences with state-of-the-art deep learning techniques for microscopy image analysis and to introduce them to tools and frameworks that facilitate independent application of the learned material after the course.

Learn and apply software engineering principles to develop Python projects addressing life science problems. Projects will be expanded iteratively throughout the semester.

By combining hands-on and theoretical training, this course introduces commonly used optical microscopy techniques. The lab training takes place in BIOP Core Facility and provides access to use microscopes and image biological samples. Theoretical part covers their basic operation principles.

Introduction to the basic techniques of image processing. Introduction to the development of image-processing software and to prototyping using Jupyter notebooks. Application to real-world examples in industrial vision and biomedical imaging.

Introduction to the basic operational principles of the most commonly used optical microscopes such as widefield and fluorescence. Introduction to essential microscopy components such as lenses, objectives, filters, cameras and light sources.