Titlebook: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; Third International M. Jorge Cardoso,Tal Ar

T-Lymphocyte

书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影响因子(影响力)




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影响因子(影响力)学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support网络公开度




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support网络公开度学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引频次




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引频次学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support读者反馈




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support读者反馈学科排名

parasite

灰姑娘

HAIRY

Left Atrium Segmentation in CT Volumes with Fully Convolutional Networksystems. This paper presents an approach to automatically segmenting left atrium in 3D CT volumes using fully convolutional neural networks (FCNs). We train FCN for automatic segmentation of the left atrium, and then refine the segmentation results of the FCN using the knowledge of the left ventricle

生存环境

3D Randomized Connection Network with Graph-Based Inferencency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are employed as network units to capture the long-term and short-term 3D properties respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we furt

最高峰

最高峰

municipality

Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Imagesjury detection system, we propose a localization framework for the cervical spine in X-ray images. The proposed framework employs a segmentation approach to solve the localization problem. As the cervical spine is a single connected component, we introduce a novel region-aware loss function for trai

吸引人的花招

cochlea