Echo-POSED learns an SO(3)×SO(3) pose representation via self-supervised equivariance to probe motion and invariance to cardiac phase from 2D slices of 3D echocardiography volumes, reporting 8.2° mean angular error in intra-patient guidance simulations.
Frontiers in Cardiovascular Medicine10, 1056055 (Feb 2023)
2 Pith papers cite this work. Polarity classification is still indexing.
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AMN fuses Swin Transformer and ResNet-50 via adaptive gating and trains with focal, boundary, and uncertainty losses to reach 0.82 mean Dice on the seven-class CoNIC benchmark.
citing papers explorer
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Echo-POSED: Geometric Self-Distillation for Echocardiography Guidance
Echo-POSED learns an SO(3)×SO(3) pose representation via self-supervised equivariance to probe motion and invariance to cardiac phase from 2D slices of 3D echocardiography volumes, reporting 8.2° mean angular error in intra-patient guidance simulations.
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AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
AMN fuses Swin Transformer and ResNet-50 via adaptive gating and trains with focal, boundary, and uncertainty losses to reach 0.82 mean Dice on the seven-class CoNIC benchmark.