Titlebook: ;

Clique

书目名称Graph Learning in Medical Imaging影响因子(影响力)




书目名称Graph Learning in Medical Imaging影响因子(影响力)学科排名




书目名称Graph Learning in Medical Imaging网络公开度




书目名称Graph Learning in Medical Imaging网络公开度学科排名




书目名称Graph Learning in Medical Imaging被引频次




书目名称Graph Learning in Medical Imaging被引频次学科排名




书目名称Graph Learning in Medical Imaging年度引用




书目名称Graph Learning in Medical Imaging年度引用学科排名




书目名称Graph Learning in Medical Imaging读者反馈




书目名称Graph Learning in Medical Imaging读者反馈学科排名

表示问

https://doi.org/10.1007/978-3-322-85959-4and ultimately avoiding the use of atlases and any registration method. We evaluate DeepBundle using data from the Human Connectome Project. Experimental results demonstrate the advantages of DeepBundle and suggest that the geometric features extracted from each fiber tract can be used to effectively parcellate the fiber tracts.

相容

https://doi.org/10.1007/978-3-322-80069-5tion demonstrates that our model is implicitly consistent with the pixel-wise segmentation labels, which indicates our model can identify the region of interests without relying on the pixel-wise labels.

Impugn

https://doi.org/10.1007/978-3-662-12498-7ew loss function that learns the geometrical relationships between the landmarks in the form of a root/leaf structure. We evaluate our approach on 49 CBCT scans of patients and achieve an average detection error of 1.75 ± 0.91 mm. Experimental results show that our approach overperforms the related methods in the term of accuracy.

易于交谈

Versicherung und Risikoforschung10 was achieved for DDSM and 0.893 for BSSA. The results indicate that graph models can capture texture features capable of identifying masses located in dense tissues, and help improve computer-aided detection systems.

实施生效

实施生效

放逐

Weakly- and Semi-supervised Graph CNN for Identifying Basal Cell Carcinoma on Pathological Images,tion demonstrates that our model is implicitly consistent with the pixel-wise segmentation labels, which indicates our model can identify the region of interests without relying on the pixel-wise labels.

抚育

中子