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Titlebook: Medical Imaging and Computer-Aided Diagnosis; Proceedings of 2022 Ruidan Su,Yudong Zhang,Alejandro F Frangi Conference proceedings 2023 Th

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發(fā)表于 2025-3-25 04:33:28 | 只看該作者
Augmented Reality Applications for Image-Guided Robotic Interventions Using Deep Learning Algorithmstage, we use an optimized convolutional neural network (CNN) which warps a set of labels from the moving image in contrast to their counterparts in the fixed image. To this end, we compare the loss functions between warped moving labels and fixed labels with respect to the ground truth. Finally, we
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發(fā)表于 2025-3-25 07:52:41 | 只看該作者
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發(fā)表于 2025-3-25 23:35:23 | 只看該作者
2.5D Lightweight Network Integrating Multi-scale Semantic Features for Liver Tumor Segmentationmentation tasks by 2.4%, while reducing amount of parameters by 57.5%. Both quantitative and qualitative results illustrated the MAA-Net can effectively address with the limitation of small tumors, and some tumors are on the edge.
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發(fā)表于 2025-3-26 02:35:44 | 只看該作者
A Change Detection with Machine Learning Approach for Medical Image Analysishout knowing the extent of their nature; the entire feature space is divided into classes using proximity or similarity criteria. After finishing the process of class identification, properties will be associated with them in relation to the known descriptions. At the end of the procedure, factorial
27#
發(fā)表于 2025-3-26 08:05:53 | 只看該作者
Anton Vasiliuk,Daria Frolova,Mikhail Belyaev,Boris Shirokikhssibility, and others..?.This book is intended for researchers, scientists, engineers and postgraduates or above with interests in robotics and advanced machine tools technology such as parallel kinematics machines (PKMs). .?.Xinjun Liu .and .Jinsong Wang., professors, work at The Institute of Manuf
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發(fā)表于 2025-3-26 09:44:52 | 只看該作者
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發(fā)表于 2025-3-26 16:25:19 | 只看該作者
30#
發(fā)表于 2025-3-26 20:27:30 | 只看該作者
Manal Hamda,Btihal El Ghali,Imane Hilal,Omar El Midaoui,Nabil Ngote,Bahia El Abdi,Kawtar Megdicheseveral applications of speculative computation. The results demonstrate that our support for speculative computation adds expressive and computational power to Multilisp, with observed performance improvement as great as 26 times over conventional approaches to parallel computation.
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