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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

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书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
副标题26th International C
编辑Hayit Greenspan,Anant Madabhushi,Russell Taylor
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay
描述.The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. ..The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections:..Part I: Machine learning with limited supervision and machine learning – transfer learning;..Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; ..Part III: Machine learning – explainability, bias and uncertainty; image segmentation; ..Part IV: Image segmentation; ..Part V: Computer-aided diagnosis; ..Part VI: Computer-aided diagnosis; computational pathology; .Part VII: Clinical applications – abdomen; clinicalapplications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applicatio
出版日期Conference proceedings 2023
关键词applied computing; life and medical sciences; computational biology; computer vision; computing methodol
版次1
doihttps://doi.org/10.1007/978-3-031-43999-5
isbn_softcover978-3-031-43998-8
isbn_ebook978-3-031-43999-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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发表于 2025-3-21 22:04:48 | 显示全部楼层
978-3-031-43998-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023978-3-031-43999-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Learned Alternating Minimization Algorithm for Dual-Domain Sparse-View CT Reconstructioncture into the design of LAMA. We show that LAMA substantially reduces network complexity, improves memory efficiency and reconstruction accuracy, and is provably convergent for reliable reconstructions. Extensive numerical experiments demonstrate that LAMA outperforms existing methods by a wide margin on multiple benchmark CT datasets.
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CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?can be reused for reconstruction tasks with different undersampling rates. We demonstrated, through extensive numerical and visual experiments, that the proposed CDiffMR can achieve comparable or even superior reconstruction results than state-of-the-art models. Compared to the diffusion model-based
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Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstructionated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT
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