绊住
发表于 2025-3-30 12:13:14
https://doi.org/10.1007/978-3-531-92409-0ration methods. Our method integrates a vessel segmentation network into the image-to-image translation task by extending the CycleGAN framework. The segmentation network is inserted prior to aUNet vision transformer generator network and serves as a shared representation between both domains. We re
多余
发表于 2025-3-30 15:44:17
https://doi.org/10.1007/978-3-531-92409-0 body. However, a medical expert would include in their reasoning also the context around the organ. In this work, we propose reproducing this human behavior by enhancing the conventional multi-class segmentation pipeline with additional anatomical information. We apply this concept to a ventral org
争吵加
发表于 2025-3-30 17:34:51
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CORE
发表于 2025-3-30 23:26:08
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不断的变动
发表于 2025-3-31 02:31:38
https://doi.org/10.1007/978-94-6300-779-5 stroke. We present a deep learning model to estimate the cerebral vessel tree from the NCCT instead of subsequently performed contrast-enhanced imaging techniques, e.g. computed tomography angiography (CTA). We employ a volumetric sliding window approach and feed the patches to a 3D U-Net. This U-N
高度表
发表于 2025-3-31 06:07:06
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周兴旺
发表于 2025-3-31 11:10:33
Religiosity and Mental Health in Islamty between brain regions and planning operations on the brain. XTRACT is a recently developed program for automatically delineating 42 major WM tracts in high-quality DWI data. However, acquiring such data is time-consuming, limiting its clinical application. In this work, we propose using a deep ne
一大块
发表于 2025-3-31 16:26:51
Religiosity and Mental Health in Islam is most commonly used as starting point for planning. However, native CT (nCT) is required for accurate dosimetry computations. In thiswork,we propose an in-silico method to remove the contrast agent from ceCT images so that the Hounsfield Units (HU) would be similar to those in nCT. Two approaches
Condescending
发表于 2025-3-31 19:28:11
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伪造
发表于 2025-3-31 22:37:49
Bildverarbeitung für die Medizin 2023978-3-658-41657-7Series ISSN 1431-472X Series E-ISSN 2628-8958