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Titlebook: Applications of Medical Artificial Intelligence; First International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2022 T

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41#
發(fā)表于 2025-3-28 15:30:11 | 只看該作者
42#
發(fā)表于 2025-3-28 19:19:15 | 只看該作者
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發(fā)表于 2025-3-28 23:04:38 | 只看該作者
44#
發(fā)表于 2025-3-29 04:17:55 | 只看該作者
Seasonal Anoestrus in Wild Sows using both simulated and clinical study data in a multitask regression setting. The results not only show promising performance in detecting near and far out-of-distribution data cases, but may also suggest the improved performance in predicting GA growth rate for in-distribution data.
45#
發(fā)表于 2025-3-29 09:37:52 | 只看該作者
,Deep Learning Meets Computational Fluid Dynamics to?Assess CAD in?CCTA,cquired scans, revealed that the suggested segmentation approaches not only outperform state-of-the-art nnU-Nets, but also lead to the blood-flow parameters which are in strong agreement with those elaborated for the ground-truth delineations.
46#
發(fā)表于 2025-3-29 13:43:02 | 只看該作者
Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence, using both simulated and clinical study data in a multitask regression setting. The results not only show promising performance in detecting near and far out-of-distribution data cases, but may also suggest the improved performance in predicting GA growth rate for in-distribution data.
47#
發(fā)表于 2025-3-29 17:16:02 | 只看該作者
Oscar Escobar,Eliana M. Perez-Garcia levels. Furthermore, we summarize some techniques to alleviate these biases for the development of fair deep learning models. We present a learning task to classify negative and positive screening mammographies and analyze the influence of biases in the performance of the algorithm.
48#
發(fā)表于 2025-3-29 23:41:51 | 只看該作者
49#
發(fā)表于 2025-3-30 01:44:04 | 只看該作者
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發(fā)表于 2025-3-30 05:57:41 | 只看該作者
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