Carolina Wählby

Professor at Department of Information Technology; Vi3; Image Analysis

Telephone:: +46 18 471 34 73
E-mail:: carolina.wahlby@it.uu.se
Visiting address:: Hus 10, Regementsvägen 10
Postal address:: Box 337
751 05 UPPSALA

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Short presentation

I'm a professor in quantitative microscopy at the Dept. of Information Technology and SciLifeLab. One might imagine that I have microscopes in my lab, but we work completely digitally and develop computational image analysis approaches measure and count things in microscopy images. The goal with the image analysis varies from evaluating the effect of drugs to developing AI-based methods for decision support in medical diagnostics. Our TissUUmaps project maps gene expression in tissue.

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Biography

I received a MSc in Molecular Biothechnology in 1998, and during my MSc thesis work at the Karolinska Institute I was fascinated by how cells can be studied using microscopy, and continued as a PhD student in digital image processing at Uppsala University, focusing on methods for finding cells and extracting quantitative measurements from digital microscopy data. After completeing my PhD in 2003, I did a postdoc in genetics and pathology, with emphasis on methods development. I joined the Broad Institute of Harvard and MIT i USA in 2009, and worked with algorithms for analysis of large scale experiments on model organisms such as C. elegans worms and zebrafish to evaluate the effect of new potential drugs. I returned to Sweden and SciLifeLab and became full professor in quantitative microscopy at the Centre for image analysis, Dept. of Information Technology, in 2014. I am scientific director for the SciLifeLab bioimage informatics facility, BIIF, providing research support on image analysis, and I'm part of the steering group of the national Wallenberg program in data driven life science, DDLS. The use of AI in image-based diagnostics interests me, and I have been part of the borad of MedTech4Health - Analytic Imaging Diagnostics Arena, AIDA since its start in 2017.

Research

Digital image processing and analysis is all about interpreting image data using a computer. As an example, we can train a computer to recognize different family members from our holiday photos or automatically decipher the license plate of a car at a car toll. My research group develops digital image analysis methods for automated analysis and extraction of quantitative information from digital image data collected via different types of microscopy. The goal of the analysis is often to measure changes in color, shape, pattern or size from large numbers of images collected by automated microscopy systems, for example to evaluate how different drugs affect cells or model organisms in a laboratory environment. We also quantify morphological changes in tissue samples, using deep convolutional neural networks (a branch of AI, artificial intelligence) aiming to diagnose disease or better understand how the body responds to different treatments. We collaborate with researchers from the life sciences and medicine, and develop methods that can answer important questions in a robust, fast, and reproducible way.