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		<title>Synthetic rT1/T2 Maps Give U-Net Networks a Sharper View of Brain Tumors</title>
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		<pubDate>Sun, 18 Oct 2026 04:03:19 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[brain tumor segmentation]]></category>
		<category><![CDATA[BraTS dataset]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[glioma]]></category>
		<category><![CDATA[InceptionV3]]></category>
		<category><![CDATA[Medical Imaging]]></category>
		<category><![CDATA[MRI]]></category>
		<category><![CDATA[ResNet]]></category>
		<category><![CDATA[rT1/T2 maps]]></category>
		<category><![CDATA[TOPSIS]]></category>
		<category><![CDATA[U-Net]]></category>
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					<description><![CDATA[A new study shows that synthetic relative T1/T2 maps reconstructed from routine MRI sequences modestly but consistently improve U-Net-based segmentation of glioma sub-regions across six backbone architectures.]]></description>
		
		
		
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