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	<title>cross-entropy loss &#8211; BIOENGINEER.ORG</title>
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		<title>AI Learns to Measure New Bone Growth in Scaffolds, But Standard Metrics Mislead</title>
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		<pubDate>Mon, 05 Oct 2026 10:28:44 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Biomedical Imaging]]></category>
		<category><![CDATA[Bland-Altman analysis]]></category>
		<category><![CDATA[Bone regeneration]]></category>
		<category><![CDATA[cross-entropy loss]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Dice coefficient]]></category>
		<category><![CDATA[evaluation metrics]]></category>
		<category><![CDATA[hydroxyapatite scaffold]]></category>
		<category><![CDATA[loss functions]]></category>
		<category><![CDATA[micro-CT]]></category>
		<category><![CDATA[semantic segmentation]]></category>
		<category><![CDATA[U-Net]]></category>
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					<description><![CDATA[Researchers trained three U-Net models to segment bone ingrowth in micro-CT scaffold images and found that standard segmentation metrics can badly misrepresent performance, prompting a new application-specific measure called delta BGF.]]></description>
		
		
		
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