CSCS selects initial annotation samples for 3D medical segmentation by combining self-supervised typicality and reconstruction uncertainty through a closed-form pacing rule based on the Difficulty-Coverage Ratio.
arXiv:2106.13731 (2021)
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PanoSAMic modifies SAM with multi-stage feature encoding, spatio-modal fusion, spherical attention, and dual-view fusion to achieve SOTA panoramic semantic segmentation on public RGB and RGB-D datasets.
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Dataset-Aware Cold-Start Active Learning for Annotation-Efficient 3D Medical Image Segmentation
CSCS selects initial annotation samples for 3D medical segmentation by combining self-supervised typicality and reconstruction uncertainty through a closed-form pacing rule based on the Difficulty-Coverage Ratio.
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PanoSAMic: Panoramic Image Segmentation from SAM Feature Encoding and Dual View Fusion
PanoSAMic modifies SAM with multi-stage feature encoding, spatio-modal fusion, spherical attention, and dual-view fusion to achieve SOTA panoramic semantic segmentation on public RGB and RGB-D datasets.