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

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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

organism

978-3-031-43989-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

樹膠

conservative

Anticoagulants

Hypopnea

DisAsymNet: Disentanglement of?Asymmetrical Abnormality on?Bilateral Mammograms Using Self-adversariat our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.

邊緣

A Spatial-Temporally Adaptive PINN Framework for?3D Bi-Ventricular Electrophysiological Simulations enable solutions over longer time domains. We experimentally demonstrated the effectiveness of the presented PINN framework to obtain the complete forward and inverse EP solutions over the 3D bi-ventricular geometry, which is otherwise not possible with vanilla PINN frameworks.

SMART

Acetabulum

Source-Free Domain Adaptation for?Medical Image Segmentation via?Prototype-Anchored Feature Alignmenbi-directional transport to align the target features with class prototypes by minimizing its expected cost. On top of that, a contrastive learning stage is further devised to utilize those pixels with unreliable predictions for a more compact target feature distribution. Extensive experiments on a

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