<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ophthalmology on Rachid Youven Zeghlache</title><link>https://youvenz.github.io/tags/ophthalmology/</link><description>Recent content in Ophthalmology on Rachid Youven Zeghlache</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://youvenz.github.io/tags/ophthalmology/index.xml" rel="self" type="application/rss+xml"/><item><title>Medical Image Analysis</title><link>https://youvenz.github.io/research/medical-image-analysis/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://youvenz.github.io/research/medical-image-analysis/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;Medical image analysis is the computational backbone of my research. Before building longitudinal or predictive models, we need robust methods for understanding what is in a single image: detecting lesions, quantifying biomarkers, segmenting anatomical structures. In my work the primary modalities are ophthalmological.&lt;/p&gt;
&lt;h2 id="imaging-modalities"&gt;Imaging modalities&lt;/h2&gt;
&lt;h3 id="colour-fundus-photography"&gt;Colour Fundus Photography&lt;/h3&gt;
&lt;p&gt;Wide-field retinal photographs capture the optic disc, macula, vessels, and peripheral retina. DR grading, vessel segmentation, and optic disc detection are well-established tasks. I use fundus images as the primary modality in several longitudinal and progression prediction pipelines.&lt;/p&gt;</description></item></channel></rss>