Riemannian Geometric Statistics in Medical Image Analysis (Seminar)

If you are interested, please write a short e-mail to jonathan.schwarz@iwr.uni-heidelberg.de.

Please write a mail to jonathan.schwarz@iwr.uni-heidelberg.de until October 31st, 2020 if you want to participate.

The Proseminar will take place every Friday from 4-6 pm starting in November.

If you want to participate in the seminar, you have to:

• give a talk (using the board or slides …)
• hand in a written summary latest a week after your talk
• attend the other talks

If there are questions upcoming during the preparation of the talk, please don't hesitate to ask.

You have to give a talk on your topic

• The talk will be done in presents or via zoom (depending on the Covid situation)
• You are free to choose any format for your presentation (slides or writing some notes during the presentation)
• The talk should last between 20 and 30 minutes.
• There will be a discussion session after your talk of around 5 to 10 minutes

Send the slides as PDF to jonathan.schwarz@iwr.uni-heidelberg.de the latest the day before your talk.

You have to hand in a written summary of your topic the latest 7 days after your talk.

• The summary should be created with LaTeX and should be handed in as a Pdf file.
• I recommend the LNCS LaTeX Template, but you don't have to use it.
• The summary should be between 2 and 4 pages long

Send the summary as PDF to jonathan.schwarz@iwr.uni-heidelberg.de.

• Submission slides: at least one day in advance of your presentation

• Submission summary: one week after of your presentation

The Seminar will be based on the book *Riemannian Geometric Statistics in Medical Image Analysis*

• SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering by Kuang, Yun and Park, Journal of Global Optimization, 2015, pdf
• k-MLE: A fast algorithm for learning statistical mixture models by Frank Nielsen, arXiv preprint, 2012, pdf
• Wasserstein Dictionary Learning: Optimal Transport-Based Unsupervised Nonlinear Dictionary Learning by Schmitz et al., SIAM Journal on Imaging Sciences, 2018, pdf
• Ising and Potts models on the hypercubic lattice by Duminil-Copin H., arXiv preprint arXiv:1707.00520, 2017 link (only one of the chapters 1, 2, 4, 5.1, 6.1)
• Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem by Shun-ichi Amari et. al., Information Geometry, Springer, 2018, pdf
• Escort Evolutionary Game Theory by Marc Harper, arXiv, 2012, pdf