Filtered 1:1 synthetic MRI augmentation improved MobileViTV2 tumour classification accuracy by 1.02% on BRISC 2025 while providing no benefit to random forest and non-significant gains for CNN.
Brain imaging generation with latent diffusion models,
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TriPF-Net synthesizes hepatobiliary phase MRI images from pre-HBP sequences using adaptive triple-phase fusion and clinical variables, reporting MAE 10.65-12.41, PSNR 23.11-23.27, and SSIM 0.76-0.78 on internal and external datasets.
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.
citing papers explorer
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Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025
Filtered 1:1 synthetic MRI augmentation improved MobileViTV2 tumour classification accuracy by 1.02% on BRISC 2025 while providing no benefit to random forest and non-significant gains for CNN.
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Triple-Phase Sequential Fusion Network for Hepatobiliary Phase Liver MRI Synthesis
TriPF-Net synthesizes hepatobiliary phase MRI images from pre-HBP sequences using adaptive triple-phase fusion and clinical variables, reporting MAE 10.65-12.41, PSNR 23.11-23.27, and SSIM 0.76-0.78 on internal and external datasets.
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DustNET: enabling machine learning and AI models of dusty plasmas
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.