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Titlebook: Machine Learning for Medical Image Reconstruction; Third International Farah Deeba,Patricia Johnson,Jong Chul Ye Conference proceedings 20

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31#
發(fā)表于 2025-3-26 21:21:43 | 只看該作者
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發(fā)表于 2025-3-27 03:03:55 | 只看該作者
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發(fā)表于 2025-3-27 07:09:18 | 只看該作者
Improving PET-CT Image Segmentation via Deep Multi-modality Data Augmentationtoring of cancers. Deep learning based computer aided diagnosis systems have achieved high accuracy on tumor segmentation on PET-CT images in recent years. PET images can be used to detect functional structures such as tumors, whilst CT images provide complementary anatomical information. As for tum
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發(fā)表于 2025-3-27 10:13:46 | 只看該作者
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發(fā)表于 2025-3-27 22:57:55 | 只看該作者
A Deep Prior Approach to Magnetic Particle Imagingprocessing tasks, it has been shown to be applicable to inverse problems. In this work, we investigate the DIP approach in the context of MPI. Its behavior is illustrated and compared to standard reconstruction methods on a 2D phantom data set obtained from the Bruker preclinical MPI system.
38#
發(fā)表于 2025-3-28 05:18:38 | 只看該作者
0302-9743 20, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually...The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and d
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發(fā)表于 2025-3-28 07:58:33 | 只看該作者
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