TL;DR
Results indicate that LiT‑WSAG delivers robust 3D liver tumor segmentation from 2D supervision, balancing boundary accuracy, noise robustness, and computational efficiency.
A novel residual “Enhancement-Calibration” U-Net architecture, termed REC-UNet, which achieves high overall segmentation accuracy across diverse lesion sizes and contrast conditions without relying on explicit size-stratified optimization.
F focal-modulation-driven global context with deformable-convolution-based local adaptation produces boundary-refined pancreatic tumor segmentations on CT, which improves boundary fidelity and provides a transparent layer of review that may support treatment planning and longitudinal monitoring.
VGG16-MCA UNet is presented, a hybrid architecture pairing an ImageNet-pretrained VGG16 encoder with a decoder in which a Multi-Channel Attention (MCA) module recalibrates features after each skip-connection fusion, trained with the Focal Tversky loss to counter severe foreground-background imbalance.
DE-GAN is proposed, a contrast-enhancing conditional GAN that combines input-adaptive dynamic convolutions, style-aware feature mixing, and coordinate encoding to synthesize slice-adaptive FLAIR images to synthesize slice-adaptive FLAIR images.
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
CondSEB-Net improves adaptive feature representation and boundary-related segmentation performance while preserving the self-configuring pipeline of nnU-Net v2, and provides benchmark-level evidence for the proposed framework.
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