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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LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping
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 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.
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Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
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.