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	<title>Medical imaging AI &#8211; BIOENGINEER.ORG</title>
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		<title>Optimized U-Net Model for Retinal Disc Segmentation</title>
		<link>https://bioengineer.org/optimized-u-net-model-for-retinal-disc-segmentation/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sat, 24 Jan 2026 00:34:45 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Automated Optic Disc Segmentation]]></category>
		<category><![CDATA[Customized U-Net Architecture]]></category>
		<category><![CDATA[Medical imaging AI]]></category>
		<category><![CDATA[Ophthalmology Diagnostics]]></category>
		<category><![CDATA[Retinal Fundus Images]]></category>
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					<description><![CDATA[In a groundbreaking study, researchers led by Skouta, A., along with Elmoufidi, A., Jai-Andaloussi, S. and their talented team, have unveiled a customized U-Net architecture specifically designed for the automated segmentation of optic discs in retinal fundus images. This pioneering approach seeks to enhance diagnostic accuracy and potentially revolutionize how we assess and monitor retinal [&#8230;]]]></description>
		
		
		
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		<title>Revolutionizing Retinal Vessel Classification with Y-Net Networks</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 30 Dec 2025 09:07:27 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Artery/vein differentiation]]></category>
		<category><![CDATA[Deep learning in ophthalmology]]></category>
		<category><![CDATA[Medical imaging AI]]></category>
		<category><![CDATA[Retinal vessel classification]]></category>
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					<description><![CDATA[In a groundbreaking study set to advance the field of medical imaging, researchers have unveiled a novel approach to enhance the classification of arteries and veins in retinal images using advanced Y-Net convolutional networks. The significance of this work cannot be overstated as it aims to elevate the accuracy and reliability of retinal imaging diagnosis. [&#8230;]]]></description>
		
		
		
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