SwinUNETR outperforms 3D UNet with Dice scores up to 0.902 on larger gland subsets using mixed-cohort five-fold training, while UNETR performs poorly on the same subsets.
Datasets – Describing the imaging sources, voxel spacing, and inert-reader class labels for the 546-volume cohort
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Improving Prostate Gland Segmentation Using Transformer based Architectures
SwinUNETR outperforms 3D UNet with Dice scores up to 0.902 on larger gland subsets using mixed-cohort five-fold training, while UNETR performs poorly on the same subsets.