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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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

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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

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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.

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

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

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