Kunstig intelligens i helsetjenesten - Hvordan påvirker dette legestudiet?

Kunstig intelligens er på full fart inn i norsk helsevesen. Hvilken kompetanse vil vi trenge fremover, og hvordan tilegner vi oss den? Må vi tenke nytt om kompetanse og utdanning? Presentasjon ("Hvordan påvirker dette legestudiet?") ved Tekna seminar i samarbeid med Teknologirådet, 17. januar 2018, Litteraturhuset, Oslo. Se link Program …

Erasmus+ OERCompBiomed Kick-off Workshop

Erasmus+ Strategic Partnership for higher education OPEN EDUCATIONAL RESOURCES IN COMPUTATIONAL BIOMEDICINE

OERCompBiomed Start: 01-09-2017 - End: 31-08-2020

EU Grant: 363670 EUR

OERCompBiomed delivers four integrated intellectual outputs (IOs) that provide novel learning material in the form of three OERs/courses (Biomedical ethics, Translational digital pathology and Introduction to Computational Biomedicine …

CNN kidney segmentation in DCE-MRI

Alexander Selvikvåg Lundervold, Jarle Rørvik, and Arvid Lundervold. Fast semi-supervised segmentation of the kidneys in DCE-MRI using convolutional neural networks and transfer learnin. Presented at the 2nd International Scientific Symposium:​Functional Renal Imaging: Where Physiology, Nephrology, Radiology and Physics Meet,​ Berlin, October 11-13, 2017. Co-organized by the European Cooperation in …

Brain vasculature

M. Kocinski, A. Materka, A. Deistung, J. Reichenbach, A. Lundervold. Towards multi-scale personalized modeling of brain vasculature based on magnetic resonance image processing. International Conference on Systems, Signals and Image Processing (IWSSIP), 22-24 May 2017, Poznan, Poland. See link IEEE Xplore and the PDF.

Kocinski_etal_IWSSIP_2017

Reproducible Data Analysis in Jupyter from Jake Vanderplas (jakevdp)

Jupyter notebooks provide a useful environment for interactive exploration of data. A common question I get, though, is how you can progress from this nonlinear, interactive, trial-and-error style of exploration to a more linear and reproducible analysis based on organized, packaged, and tested code. This series of videos presents a case study in how I personally approach reproducible data analysis within the Jupyter notebook.

Each video is approximately 5-8 minutes; the videos are available in a YouTube Playlist. Alternatively, below you can find the videos with some description and links to relevant resources

Launches site

Well. Finally got around to putting this website together. Neat thing about it - powered by Jekyll and I can use Markdown to author my posts. It actually is a lot easier than I thought it was going to be - thanks to hankquinlan.github.io!

Restructured the blog (2017-12-31) according to …