<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cryo-ET | Farhan Noor Dehan</title><link>https://dehan001.github.io/tags/cryo-et/</link><atom:link href="https://dehan001.github.io/tags/cryo-et/index.xml" rel="self" type="application/rss+xml"/><description>Cryo-ET</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 15 Dec 2025 00:00:00 +0000</lastBuildDate><image><url>https://dehan001.github.io/media/icon_hu7729264130191091259.png</url><title>Cryo-ET</title><link>https://dehan001.github.io/tags/cryo-et/</link></image><item><title>Cryo-ET Filament Segmentation</title><link>https://dehan001.github.io/project/cryo-et-filament-segmentation/</link><pubDate>Mon, 15 Dec 2025 00:00:00 +0000</pubDate><guid>https://dehan001.github.io/project/cryo-et-filament-segmentation/</guid><description>&lt;p>I contributed to the development and evaluation of semantic-segmentation pipelines for actin filament networks in simulated cryo-electron tomograms, including analysis across noise conditions and model architectures.&lt;/p></description></item><item><title>Evaluating Deep Learning Architectures for Actin Filament Segmentation Under Varying Noise Conditions in Simulated Cryo-ET Tomograms</title><link>https://dehan001.github.io/publication/actin-filament-segmentation/</link><pubDate>Mon, 15 Dec 2025 00:00:00 +0000</pubDate><guid>https://dehan001.github.io/publication/actin-filament-segmentation/</guid><description>&lt;p>This work evaluates deep-learning architectures for segmenting actin filament networks in simulated cryo-electron tomograms, with emphasis on robustness across varying noise conditions.&lt;/p></description></item></channel></rss>