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.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp
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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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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.