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	<title>multi-organ segmentation &#8211; BIOENGINEER.ORG</title>
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		<title>Dual-Teacher AI Learns to Segment Abdominal Organs From Scarce Labels and Knows When to Stop</title>
		<link>https://bioengineer.org/dual-teacher-ai-learns-to-segment-abdominal-organs-from-scarce-labels-and-knows-when-to-stop/</link>
		
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		<pubDate>Mon, 05 Oct 2026 10:43:55 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[3D Res-UNet]]></category>
		<category><![CDATA[abdominal CT]]></category>
		<category><![CDATA[early exit inference]]></category>
		<category><![CDATA[FLARE22 benchmark]]></category>
		<category><![CDATA[Mean Teacher]]></category>
		<category><![CDATA[Medical imaging AI]]></category>
		<category><![CDATA[multi-organ segmentation]]></category>
		<category><![CDATA[pseudo-labels]]></category>
		<category><![CDATA[semi-supervised learning]]></category>
		<category><![CDATA[topology consistency]]></category>
		<category><![CDATA[uncertainty estimation]]></category>
		<category><![CDATA[URDT-Net]]></category>
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					<description><![CDATA[A new semi-supervised AI framework called URDT-Net combines dual-teacher learning, topology-aware training, and an early-exit mechanism to segment abdominal organs from CT scans more accurately while cutting inference time by about a quarter.]]></description>
		
		
		
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