歌唱队
发表于 2025-3-25 03:40:19
Jackie Duckworth newly available data to mitigate model drift. Simply fine-tuning on new datasets results in “catastrophic forgetting”, and cannot adapt to variations of view labels between sites. Alternatively, collecting all data on a single server and re-training may not be feasible as data sharing agreements ma
troponins
发表于 2025-3-25 09:57:17
Les Garnertrasound (US) examinations often fail to identify these features. This study presents an AI-driven tool to assist clinicians in standardizing fetal facial axes/planes in 3D US, reducing sonographer workload and facilitating the facial evaluation. Our network, structured into three blocks-feature ext
NOCT
发表于 2025-3-25 13:07:12
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人类
发表于 2025-3-25 18:29:05
Ian Bullockion of the LV. Training deep neural networks to automate such measurements is challenging because the gold standard clinical labels are noisy due to inherent observer variability. Also, the labels are only available for at most two time instances in the cine series, end-diastole (ED) and end-systole
Schlemms-Canal
发表于 2025-3-25 23:36:09
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Project
发表于 2025-3-26 02:12:13
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Cpap155
发表于 2025-3-26 05:35:25
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不利
发表于 2025-3-26 09:50:38
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pericardium
发表于 2025-3-26 14:36:53
mulation data generated by finite element analysis is employed to train the network. However, the computational cost and complexity of modeling the wave propagation contribute to the limited practicality of supervised methods. In this paper, we present an unsupervised physics-inspired learning metho
哑巴
发表于 2025-3-26 20:06:13
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