Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

手榴彈

Domain-Invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing edge, which are important guidance information for accurate brain extraction of developing macaques from 0 to 36 months of age. Specifically, we introduce signed distance map (SDM) and center of gravity distance map (CGDM) based on the intermediate segmentation results and fuse their information by

amorphous

Progesterone

Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generative-layer graph convolution networks (GCNs) which have the capability to model complex indirect connections in brain connectivity. The discriminator of MGCN-GAN is a single multi-layer GCN which aims to distinguish predicted SC from real SC. To overcome the inherent unstable behavior of GAN, we designe

facilitate

Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprintingiminative capability among infant individuals. Then, a disentanglement strategy is proposed to separate the latent variables into identity-code, age-code, and noise-code, which not only restrains the interference from age-related developmental variance, but also captures the identity-related invaria

飾帶

Carcinogenesis

Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regressionession method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings . our method and previous reports demonstrate the effectiveness and the promise of this framework.

PRE

Minatory

Unified Brain Network with Functional and Structural Dataifold with structural data into this model. The constructed network then captures the global brain region correlation by the low-rank constraint and preserves the local structural information by manifold learning. Second, we adaptively estimate the importance of different brain regions by PageRank a

服從

Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Diural scores. Current edge weights are used to construct an initial graph and .-. the GCN. Based on the pre-trained GCN, the differences between scores replace the traditional correlation distances to evaluate edge weights. Lastly, we devise a . technique to . functional and structural information fo

牛的細(xì)微差別

Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Featurering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed meth