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

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Zhiyun Song,Xin Wang,Xiangyu Zhao,Sheng Wang,Zhenrong Shen,Zixu Zhuang,Mengjun Liu,Qian Wang,Lichi Zkt der Resilienz das vorletzte Kapitel gewidmet, bevor sich ein Ausblick in die Zukunft anschlie?t.?.Die didaktische Struktur des Lehrbuchs enth?lt neben Lernzielen, Praxisbeispielen und Fallstudien sowie Lernv978-3-658-42338-4978-3-658-42339-1

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Hadrien Reynaud,Mengyun Qiao,Mischa Dombrowski,Thomas Day,Reza Razavi,Alberto Gomez,Paul Leeson,Bernhe study area for assessing this approach is Louisiana (United States), which – being spatially quite diverse – has been intensively shaped for more than a century by the activities of the petrochemical industr978-3-658-43395-6978-3-658-43396-3Series ISSN 2625-6991 Series E-ISSN 2625-7009

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Yongsheng Pan,Feihong Liu,Caiwen Jiang,Jiawei Huang,Yong Xia,Dinggang Shen

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Huidong Xie,Bo Zhou,Xiongchao Chen,Xueqi Guo,Stephanie Thorn,Yi-Hwa Liu,Ge Wang,Albert Sinusas,Chi L

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CDiffMR: Can We Replace the?Gaussian Noise with?K-Space Undersampling for?Fast MRI?ave gained burgeoning interests as a novel group of deep learning-based generative methods. These methods seek to sample data points that belong to a target distribution from a Gaussian distribution, which has been successfully extended to MRI reconstruction. In this work, we proposed a Cold Diffusi

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Learning Deep Intensity Field for?Extremely Sparse-View CBCT Reconstructionsed generation methods represent the CT as discrete voxels, resulting in high memory requirements and limited spatial resolution due to the use of 3D decoders. In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstru

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