Claes Lundström
Linköping University
87 Papers
508 Citations
Claes Lundström is an academic researcher from Linköping University. The author has contributed to research in topics: Computer science & Digital pathology. The author has an hindex of 21, co-authored 80 publications. Previous affiliations of Claes Lundström include Karolinska Institutet.
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Papers
Measuring Domain Shift for Deep Learning in Histopathology
TL;DR: This work focuses on the internal representation learned by trained convolutional neural networks, and shows how this can be used to formulate a novel measure – the representation shift – for quantifying the magnitude of model-specific domain shift.
Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
TL;DR: The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.
173
Implementation of large-scale routine diagnostics using whole slide imaging in Sweden: Digital pathology experiences 2006-2013
TL;DR: The fact that two full-scale digital systems have been implemented and that a large portion of the primary reporting is voluntarily performed digitally shows that large-scale digitization is possible today.
168
Local Histograms for Design of Transfer Functions in Direct Volume Rendering
TL;DR: This paper uses histograms of local neighborhoods to capture tissue characteristics to perform a classification where the tissue-type certainty is treated as a second TF dimension and results in an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF.
157
Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal.
Jakub Olczak,John Pavlopoulos,Jasper Prijs,Jasper Prijs,Frank F A IJpma,Job N. Doornberg,Job N. Doornberg,Claes Lundström,Joel Hedlund,Max Gordon +9 more
TL;DR: In this paper, Artificial Intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general, and Artificial Intelligence, deep learning, and ML have been used for medical applications.
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