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

发现

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

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

相符

abysmal

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

Harridan

https://doi.org/10.1007/978-3-031-16440-8Computer Science; Informatics; Conference Proceedings; Research; Applications

平庸的人或物

进入

978-3-031-16439-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

煤渣

Medical Image Computing and Computer Assisted Intervention – MICCAI 2022978-3-031-16440-8Series ISSN 0302-9743 Series E-ISSN 1611-3349