CoralLite dataset and V-Trans-UNet baseline enable segmentation of individual corallites from μCT scans of Porites coral colonies with reported Dice scores of 0.77 on same-colony slices and 0.63 on unrelated specimens.
In: Proceedings of the IEEE/CVF winter conference on applications of computer vi- sion
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USEMA is a hybrid UNet architecture merging CNNs with scalable Mamba-like attention (SEMA) that achieves better efficiency than transformers and superior segmentation accuracy than pure CNN or Mamba models across medical imaging modalities.
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CoralLite: {\mu}CT Reconstruction of Coral Colonies from Individual Corallites
CoralLite dataset and V-Trans-UNet baseline enable segmentation of individual corallites from μCT scans of Porites coral colonies with reported Dice scores of 0.77 on same-colony slices and 0.63 on unrelated specimens.
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USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation
USEMA is a hybrid UNet architecture merging CNNs with scalable Mamba-like attention (SEMA) that achieves better efficiency than transformers and superior segmentation accuracy than pure CNN or Mamba models across medical imaging modalities.