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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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Feng Liu,Guihong Wan,Yevgeniy R. Semenov,Patrick L. Purdon
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Progression Models for Imaging Data with Longitudinal Variational Auto Encodersmethod. We then apply it to 3D MRI and FDG-PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to recover well documented patterns of structural and metabolic alterations of the brain.
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Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cogal MRI adaptation (DSMA) method for SCD progression prediction by mitigating the distribution gap between SCD and AD groups. The proposed DSMA method consists of two parallel . for MRI feature learning in the labeled source domain and unlabeled target domain, an . to locate potential disease-associa
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CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysisyers during fine-tuning. This reduces the number of parameters by over 90% with respect to the original model and therefore enables the application of large models on small datasets without overfitting. In addition, CASHformer models cognitive decline to reveal AD atrophy patterns in the temporal se
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Feature Robustness and Sex Differences in Medical Imaging: A Case Study in MRI-Based Alzheimer’s Disdataset composition, we find that CNN performance is generally improved for both male and female subjects when including more female subjects in the training dataset. We hypothesize that this might be due to inherent differences in the pathology of the two sexes. Moreover, in our analysis, the logis
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Extended Electrophysiological Source Imaging with Spatial Graph Filtersose the graph signal representation in the source space into low-, medium-, and high-frequency subspaces, and project the source signal into the graph low-frequency subspace. We further introduce a low-rank representation with temporal graph regularization in the projected space to build the ESI fra
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