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

Traction

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網(wǎng)絡(luò)公開度




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋學(xué)科排名

Cerumen

978-3-031-43998-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

NAUT

Medical Image Computing and Computer Assisted Intervention – MICCAI 2023978-3-031-43999-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

Canvas

美學(xué)

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.

Cupping

Bravura

acolyte

伴隨而來

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

HAUNT

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