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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

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樓主: Lactase
11#
發(fā)表于 2025-3-23 09:49:36 | 只看該作者
12#
發(fā)表于 2025-3-23 14:06:43 | 只看該作者
SVoRT: Iterative Transformer for?Slice-to-Volume Registration in?Fetal Brain MRIing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
13#
發(fā)表于 2025-3-23 20:49:12 | 只看該作者
Double-Uncertainty Guided Spatial and?Temporal Consistency Regularization Weighting for?Learning-Basistent with that of the student model. More importantly, instead of searching for a fixed weight, the teacher enables automatically adjusting the weights of the spatial regularization and the temporal consistency regularization by taking advantage of the transformation uncertainty and appearance unc
14#
發(fā)表于 2025-3-24 01:30:08 | 只看該作者
On the?Dataset Quality Control for?Image Registration Evaluationtasets, we identified and confirmed a small number of landmarks with potential localization errors and found that, in some cases, the landmark distribution was not ideal for an unbiased assessment of non-rigid registration errors. Under discussion, we provide some constructive suggestions for improv
15#
發(fā)表于 2025-3-24 05:07:57 | 只看該作者
Dual-Branch Squeeze-Fusion-Excitation Module for?Cross-Modality Registration of?Cardiac SPECT and?CTinvestigated before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived .-maps. DuSFE fuses the knowledge from multiple modalities to recalibrate both channel-wise and spatial features for each modality. DuSFE can be
16#
發(fā)表于 2025-3-24 09:58:25 | 只看該作者
17#
發(fā)表于 2025-3-24 11:46:08 | 只看該作者
Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning
18#
發(fā)表于 2025-3-24 17:38:41 | 只看該作者
19#
發(fā)表于 2025-3-24 19:48:28 | 只看該作者
20#
發(fā)表于 2025-3-25 00:01:00 | 只看該作者
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