CardiacNAS uses resource-aware evolutionary NAS on a cardiac-specific search space to produce a UNet variant with 93.22% DSC and 4.73 mm HD95 using only 3.58 M parameters and 14.56 GFLOPs on the ACDC dataset.
Rule mining of early diabetes symptom and applied supervised machine learning and cross validation approaches based on the most important features to predict early-stage diabetes
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Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation
CardiacNAS uses resource-aware evolutionary NAS on a cardiac-specific search space to produce a UNet variant with 93.22% DSC and 4.73 mm HD95 using only 3.58 M parameters and 14.56 GFLOPs on the ACDC dataset.