发现
发表于 2025-3-26 21:39:11
MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PETost current deep learning inter-frame motion correction works consider only the image registration problem, ignoring tracer kinetics. We propose an inter-frame Motion Correction framework with Patlak regularization (MCP-Net) to directly optimize the Patlak fitting error and further improve model per
积极词汇
发表于 2025-3-27 02:15:11
PET Denoising and Uncertainty Estimation Based on NVAE Model Using Quantile Regression Lossme from their uncertainty. As data-driven models, deep learning-based methods are sensitive to imperfect data. Thus, it is important to quantify the uncertainty, especially for positron emission tomography (PET) denoising tasks where the noise is very similar to small tumors. In this paper, we propo
BILK
发表于 2025-3-27 06:55:01
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相符
发表于 2025-3-27 10:31:36
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abysmal
发表于 2025-3-27 16:17:49
Semi-supervised Learning for Nerve Segmentation in Corneal Confocal Microscope Photographyge modeling as a proxy task on unlabeled images. After supervised fine-tuning, self-training is employed to make full use of unlabeled data. Experimental results show that our proposed method is effective and better than the supervised learning using nerve annotations with three-pixel-width dilation.
同义联想法
发表于 2025-3-27 20:22:42
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Harridan
发表于 2025-3-28 01:59:23
https://doi.org/10.1007/978-3-031-16440-8Computer Science; Informatics; Conference Proceedings; Research; Applications
平庸的人或物
发表于 2025-3-28 04:30:20
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进入
发表于 2025-3-28 08:35:56
978-3-031-16439-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
煤渣
发表于 2025-3-28 10:43:49
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022978-3-031-16440-8Series ISSN 0302-9743 Series E-ISSN 1611-3349