The Hybrid Convolutional Deep U-Net is presented, an improved U-Net that incorporates multi-scale atrous convolutions, residual upsampling branches, and refined skip connections to capture contextual features and retain precise edge details across different tumor scales.
A failure-aware cascaded deep learning framework for automated liver CT segmentation using the publicly available HCC-TACE-Seg dataset is presented and indicates that cascaded localisation and region-of-interest refinement can provide robust liver segmentation while reducing background interference and supporting uncer...
Nisha Joseph, D. Mohan, Jomy George et al.· Journal of Intelligent Decis...· 0 citations
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.
Musa Doğan, I. Ozkan· BMC Medical Imaging· 0 citations
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.
V. M. Firos, P. J. A. Alphonse, Ugo Fiore et al.· Machine Vision and Applicati...· 0 citations
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.
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.
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.
Cheng-Hong Zhang, Qiang Wei· Quantitative Imaging in Medi...· 0 citations
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