VoxelFM learns robust 3D CT visual features via DINO self-distillation that transfer effectively to seven clinical task categories using frozen backbones and lightweight heads, outperforming prior CT foundation models even on report generation.
J., Kirk, S., Lee, Y., et al.: Radiology Data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma [TCGA-LIHC] collection
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Densely connected U-Net with GAN-guided training and perceptual loss corrects respiratory motion artifacts in abdominal MRI.
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Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks
VoxelFM learns robust 3D CT visual features via DINO self-distillation that transfer effectively to seven clinical task categories using frozen backbones and lightweight heads, outperforming prior CT foundation models even on report generation.
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Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training
Densely connected U-Net with GAN-guided training and perceptual loss corrects respiratory motion artifacts in abdominal MRI.