<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Nico Schulthess | RISE-MICCAI</title><link>https://rise-miccai.org/author/nico-schulthess/</link><atom:link href="https://rise-miccai.org/author/nico-schulthess/index.xml" rel="self" type="application/rss+xml"/><description>Nico Schulthess</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sat, 09 May 2026 16:00:00 +0000</lastBuildDate><image><url>https://rise-miccai.org/media/icon_hu6736919828612716670.png</url><title>Nico Schulthess</title><link>https://rise-miccai.org/author/nico-schulthess/</link></image><item><title>Modeling DINO Embeddings for Fast and Efficient Unsupervised Anomaly Detection in Medical Imaging</title><link>https://rise-miccai.org/publication/schulthess-090526/</link><pubDate>Sat, 09 May 2026 16:00:00 +0000</pubDate><guid>https://rise-miccai.org/publication/schulthess-090526/</guid><description/></item></channel></rss>