LandSegmenter creates a task-specific foundation model for LULC mapping using weak labels from existing products, an RS adapter, text encoder, and confidence-guided fusion to achieve competitive zero-shot performance across modalities and taxonomies.
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A two-stage deep learning framework segments ten GI organs from coronal MR enterography images, achieving mean DSC of 88.99% and outperforming baselines.
SegResNet trained with assorted precision achieves Dice scores of 0.84 overall, 0.84 for tumor core, 0.90 for whole tumor, and 0.79 for enhancing tumor.
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A Two-Stage Deep Learning Framework for Segmentation of Ten Gastrointestinal Organs from Coronal MR Enterography
A two-stage deep learning framework segments ten GI organs from coronal MR enterography images, achieving mean DSC of 88.99% and outperforming baselines.