善变
发表于 2025-3-26 22:47:13
A Learning-Free Approach to Mitigate Abnormal Deformations in Medical Image Registrationhave demonstrated reasonable accuracy, they can produce abnormal deformations that introduce substantial artifacts in medical images by unrealistically modifying the shape and position of anatomical structures. These abnormal deformations may not be effectively detected or mitigated during inference
Antagonist
发表于 2025-3-27 01:44:37
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Instrumental
发表于 2025-3-27 07:36:40
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姑姑在炫耀
发表于 2025-3-27 09:53:53
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MOT
发表于 2025-3-27 16:44:40
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射手座
发表于 2025-3-27 18:00:21
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厚脸皮
发表于 2025-3-27 23:55:17
Large Deformation Registration with A Confidence-Guided Networkattention registration part to register the image with the proper receptive field. Comprehensive experimental results on the brain and liver datasets show that our proposed confidence-guided network significantly improves registration accuracy over existing methods for large deformation registration
符合你规定
发表于 2025-3-28 03:01:14
Feedback Attention for Unsupervised Cardiac Motion Estimation in 3D Echocardiographyrate its good performance in both adult and fetal echocardiography images. We introduce a novel feedback spatial transformer module where the registration outputs are used to generate a co-attention map that describes the remaining registration errors to guide the network’s spatial emphasis during D
Chagrin
发表于 2025-3-28 07:07:37
Learning Deformable Intra-Patient Liver Registration with Graph Cross-Attentions-attention. Our proposed module is a novel solution that can enhance the performance of various encoder-decoder architectures. To the best of our knowledge, this is the first application of a graph cross-attention mechanism for liver registration. We carried out the experimental validation on 20 pr
neolith
发表于 2025-3-28 13:16:49
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