Titlebook: Advances in Visual Computing; 18th International S George Bebis,Golnaz Ghiasi,Luv Kohli Conference proceedings 2023 The Editor(s) (if appli

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书目名称Advances in Visual Computing影响因子(影响力)




书目名称Advances in Visual Computing影响因子(影响力)学科排名




书目名称Advances in Visual Computing网络公开度




书目名称Advances in Visual Computing网络公开度学科排名




书目名称Advances in Visual Computing被引频次




书目名称Advances in Visual Computing被引频次学科排名




书目名称Advances in Visual Computing年度引用




书目名称Advances in Visual Computing年度引用学科排名




书目名称Advances in Visual Computing读者反馈




书目名称Advances in Visual Computing读者反馈学科排名

VERT

Deep Learning Based GABA Edited-MRS Signal Reconstructiondata from real GABA-edited ground truths. Our model achieves a 95% decrease in Mean Squared Error (MSE), a 70% decrease in Linewidth, a 450% increase in Signal to Noise Ratio (SNR), and a 42% increase in Peak Shape Score compared to the current existing method on the test set. We also illustrate our

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ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation Using Object Border Fit be used to construct a boundary map, which enables . to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that ou

虚弱的神经

A Data-Centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Inhance the accuracy of the deep-learning-based mammography segmentation model. In the first stage, we introduce a shape-based label analysis to automatically identify pectoral muscle labels with possible inconsistencies for a posterior manual review and correction. Then, in the second stage, we down

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