MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
Boundary loss for highly unbalanced segmentation
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PBE-UNet adds scale-aware aggregation and progressive boundary expansion modules to U-Net and reports better segmentation performance than prior methods on four ultrasound datasets.
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.
citing papers explorer
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MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models
MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
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PBE-UNet: A light weight Progressive Boundary-Enhanced U-Net with Scale-Aware Aggregation for Ultrasound Image Segmentation
PBE-UNet adds scale-aware aggregation and progressive boundary expansion modules to U-Net and reports better segmentation performance than prior methods on four ultrasound datasets.
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Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.