Titlebook: Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss; 12th International W Stefan Wesarg,Es

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書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss影響因子(影響力)




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss影響因子(影響力)學(xué)科排名




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss網(wǎng)絡(luò)公開度




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss被引頻次




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss被引頻次學(xué)科排名




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss年度引用




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss年度引用學(xué)科排名




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss讀者反饋




書目名稱Clinical Image-Based Procedures,Fairness of AI in Medical Imaging, and Ethical and Philosophical Iss讀者反饋學(xué)科排名

愉快么

Prostaglandins

Automatic Neurocranial Landmarks Detection from?Visible Facial Landmarks Leveraging 3D Head Priorsstatistical head model priors. We validate the proposed approach on ground truth data from 1,150 subjects, for which facial 3D and head information is available; our technique achieves a localization RMSE of 2.56?mm on average, which is of the same order as reported by high-end techniques in TMS. Ou

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Automated Orientation and?Registration of?Cone-Beam Computed Tomography Scanslow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and r

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Bias in?Unsupervised Anomaly Detection in?Brain MRInal shifts between the training and testing distributions, we shed light on the extent of these biases and their influence on anomaly detection results. Moreover, this study examines the algorithmic limitations that arise due to biases, providing valuable insights into the challenges encountered by