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.
In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)
3 Pith papers cite this work. Polarity classification is still indexing.
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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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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.