VesselRW expands sparse vessel annotations into dense probabilistic supervision via a jointly trained differentiable random walk model with uncertainty weighting and topology regularization for CNN-based subcutaneous vessel segmentation.
Arxiv article (2025)
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Dual-resolution residual architecture with boundary-aware connections, channel attention, artifact suppression, and combined Dice-Tversky plus boundary and contrastive losses improves lesion boundary precision over standard encoder-decoder models on dermoscopic benchmarks.
A top-down backward refinement network with subpixel upsampling generates crisp high-resolution organ boundaries in medical images and improves downstream segmentation and registration performance.
A dual-pathway deep learning model with attention mechanisms and multi-scale feature aggregation claims superior accuracy and fewer false positives for melanoma detection on benchmark datasets.
citing papers explorer
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VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagation
VesselRW expands sparse vessel annotations into dense probabilistic supervision via a jointly trained differentiable random walk model with uncertainty weighting and topology regularization for CNN-based subcutaneous vessel segmentation.
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DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation
Dual-resolution residual architecture with boundary-aware connections, channel attention, artifact suppression, and combined Dice-Tversky plus boundary and contrastive losses improves lesion boundary precision over standard encoder-decoder models on dermoscopic benchmarks.
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Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling
A top-down backward refinement network with subpixel upsampling generates crisp high-resolution organ boundaries in medical images and improves downstream segmentation and registration performance.
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Deeply Dual Supervised learning for melanoma recognition
A dual-pathway deep learning model with attention mechanisms and multi-scale feature aggregation claims superior accuracy and fewer false positives for melanoma detection on benchmark datasets.