Universiteit Leiden

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Computer Science & AI

Vision and imaging

On the basis of the characteristic aspects of a picture, certain computers can tell us what the picture is showing. They can learn this in the same way that young children are able to learn to recognize images. Further improving these techniques opens the way to a whole range of new applications. Biology and (bio) medical sciences offer numerous applications for computer science. We are pleased to work alongside biologists and medical scientists in identifying smart solutions for medical applications. Now and in the future, computers will be decisive in fighting a whole raft of diseases.

Deep learning and computer vision

The goal of the LIACS Media Lab (LML) at Leiden University is to conduct state-of-the-art research in the areas of deep learning, artificial intelligence and computer vision. Their research spans the dominant kinds of media information which are images, video, audio and text, or "multimedia". One of the most ubiquitous society problems is how to browse and search the vast mountain of multimedia information from diverse sources such as smartphones, digital libraries, cultural heritage collections and the Internet. Even though acquiring the multimedia information is straightforward, there currently are no effective solutions for finding multimedia information using everyday common queries. The group has an emphasis on using deep learning and computer vision methods to classify images into human-understandable text and involves using the content such as pixels in images and advanced artificial intelligence and deep neural network algorithms to determine who or what is in the image.


The bio-informatics lab aims at strengthening biological, medical, behavioral research with innovative computational, mathematical and artificial intelligence technologies. The research of the bioinformatics group focuses on research methods and workflows for analyzing, modelling and semantically integrating biomedical data. They are involved in multiple initiatives to develop multiple initiatives to develop methods and infrastructure for FAIR (Findable, Accessible, Interoperable and Reusable) data, in order to increase the value of research results and to address the growing analysis bottleneck. Another research area of the group is in biopolymer sequence analysis, which focuses on bioinformatics identification of functional structures in non-coding RNAs and viral RNA genomes. This work is done in close collaboration with wet bench research laboratories. 


The imaging group focuses on bio-imaging, image analysis and visualization. With their experience in high-throughput imaging, 3D reconstruction, cell tracking and pattern recognition, the group intends to find the relation between the information analyzed from image and other bio-molecular information resources. Furthermore, they develop new algorithms and techniques for producing images using the very latest equipment. These help create clearer three-dimensional pictures of organs and – on a scale of thousands of times smaller – body cells. These applications facilitate functional study, disease modeling and drug screening in the bio-medical field.

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