镀金
发表于 2025-3-23 09:41:54
Key Issues and Dominant Themes,ng segmentations, producing promising results across several benchmarks. In this paper, we argue that the relative failure of supervised registration approaches can in part be blamed on the use of regular U-Nets, which are jointly tasked with feature extraction, feature matching, and estimation of d
缓和
发表于 2025-3-23 17:06:47
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假装是你
发表于 2025-3-23 19:26:19
Geoffrey Lee Williams,Alan Lee Williamsprises two sequential registration networks, where the local affine network can handle small deformations, and the non-rigid network is able to align texture details further. Both networks adopt the multi-magnification structure to improve registration accuracy. We train the proposed networks separa
案发地点
发表于 2025-3-24 01:17:20
Geoffrey Lee Williams,Alan Lee Williamsn. Knowledge distillation is a technique to train a faster, smaller model by learning from cues of larger models. Mobile devices with limited resources could be key in providing effective point-of-care healthcare and motivate the search of more lightweight solutions in the deep learning based image
权宜之计
发表于 2025-3-24 04:38:00
Distinct Structural Patterns of the Human Brain: A Caveat for Registrationlinear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variability.
偏见
发表于 2025-3-24 09:47:05
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Decrepit
发表于 2025-3-24 14:11:25
978-3-031-11202-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
Evacuate
发表于 2025-3-24 17:54:46
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PHON
发表于 2025-3-24 19:02:49
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OUTRE
发表于 2025-3-25 01:14:27
https://doi.org/10.1057/9780230512320linear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variability.