最高峰
发表于 2025-3-23 11:54:28
Semantically Guided 3D Abdominal Image Registration with Deep Pyramid Feature Learning,l non-learning-based methods primarily in terms of lower computational time. However, these convolutional networks face difficulties when applied to scans with large deformations. We present a semantically guided registration network with deep pyramid feature learning that enables large deformations
NAUT
发表于 2025-3-23 17:24:02
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nutrients
发表于 2025-3-23 18:01:49
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商议
发表于 2025-3-23 23:07:22
Abstract: 3D Guidance Including Shape Sensing of a Stentgraft System,ubtraction angiography. However, this guidance requires X-ray exposure and contrast agent administration, and the depth information is missing. To overcome these drawbacks, a three-dimensional (3D) guidance approach based on tracking systems is introduced and evaluated .
鄙视
发表于 2025-3-24 05:51:02
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TEN
发表于 2025-3-24 08:11:48
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拾落穗
发表于 2025-3-24 13:47:03
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无脊椎
发表于 2025-3-24 17:47:23
Abstract: Deep Learning-based Quantification of Pulmonary Hemosiderophages in Cytology Slides,vage fluid by use of a scoring system is considered the most sensitive diagnostic method. Manual grading of macrophages, depending on the degree of cytoplasmic hemosiderin content, on whole slide images (WSI) is however monotonous and time-consuming.
evaculate
发表于 2025-3-24 21:50:11
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傲慢物
发表于 2025-3-25 00:32:36
Table Motion Detection in Interventional Coronary Angiography,cause it displays complex 3D structures of arteries as 2D X-ray projections. To overcome these limitations, 3D models or tomographic images of the arterial tree can be reconstructed from 2D projections. The 3D modeling process of the arterial tree requires accurate acquisition geometry since in many