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	<title>synthetic training data &#8211; BIOENGINEER.ORG</title>
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		<title>AI Trained on Fake Images Learns to Spot Real DNA Damage from Radiation</title>
		<link>https://bioengineer.org/ai-trained-on-fake-images-learns-to-spot-real-dna-damage-from-radiation/</link>
		
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		<pubDate>Wed, 07 Oct 2026 17:03:49 +0000</pubDate>
				<category><![CDATA[Biology]]></category>
		<category><![CDATA[biodosimetry]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[DNA double-strand breaks]]></category>
		<category><![CDATA[fluorescence microscopy]]></category>
		<category><![CDATA[ionizing radiation-induced repair foci]]></category>
		<category><![CDATA[NASA GeneLab]]></category>
		<category><![CDATA[radiation biology]]></category>
		<category><![CDATA[semantic segmentation]]></category>
		<category><![CDATA[space health]]></category>
		<category><![CDATA[synthetic training data]]></category>
		<category><![CDATA[U-Net]]></category>
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					<description><![CDATA[Researchers at the University of Malta and the University of Pittsburgh trained a modified U-Net on 95,000 synthetic microscopy images and showed it can accurately detect and count radiation-induced DNA repair foci in real mouse fibroblast images, paving the way for high-throughput biodosimetry.]]></description>
		
		
		
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