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		<title>AI Learns to Spot Diabetic Eye Disease by Generating Its Own Training Images</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[APTOS 2019]]></category>
		<category><![CDATA[class imbalance]]></category>
		<category><![CDATA[contrastive learning]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[diabetic retinopathy]]></category>
		<category><![CDATA[Diffusion models]]></category>
		<category><![CDATA[domain shift]]></category>
		<category><![CDATA[ensemble learning]]></category>
		<category><![CDATA[EyePACS]]></category>
		<category><![CDATA[fundus images]]></category>
		<category><![CDATA[Medical Imaging]]></category>
		<category><![CDATA[SimCLR]]></category>
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					<description><![CDATA[Researchers combined diffusion-generated synthetic retinal images, contrastive self-supervised pre-training, and a five-model ensemble to reach 95.1 percent accuracy in diabetic retinopathy severity grading, with the largest gains on the rarest disease stages.]]></description>
		
		
		
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