Titlebook: ;

conduct

https://doi.org/10.1007/978-3-322-85959-4r parcellation approaches rely on accurate registration between an atlas and the tractograms of an individual, however, due to large individual differences, accurate registration is hard to guarantee in practice. To resolve this issue, we propose a novel deep learning method, called DeepBundle, for

健談

https://doi.org/10.1007/978-3-7091-9214-6 information can be used to improve diagnosis. The heterogeneity of depression suggests that diverse circuit-level abnormalities in individuals lead to various symptoms. Investigating heterogeneous depression is crucial to understand disease mechanisms and provide personalised medicine. Dynamical fu

放肆的我

非秘密

https://doi.org/10.1007/978-3-322-80069-5grating patch level information into the whole image level prediction. Also, it is often difficult to obtain sufficient high-quality patch labels such as pixel-wise segmentation masks. Benefiting from the recent development of Graph-CNN (GCN), we propose a new weakly- and semi-supervised GCN archite

方舟

種類

俗艷

amygdala

Versicherung und Risikoforschungdy, a novel superpixel based graph modeling technique is proposed to extract texture features from the computer identified suspicious regions of mammograms. Graph models are constructed from specific structured superpixel patterns and used to generate feature vectors used for classifications of regi

油膏

https://doi.org/10.1007/978-3-658-02654-7move redundant information in resting-state functional magnetic resonance imaging (rs-fMRI) data via the brain functional connectivity network (BFCN) and retain good biological characteristics, it is an important method for OCD analysis. However, most existing methods ignore the relationship among s

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