填料
发表于 2025-3-25 04:31:41
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innovation
发表于 2025-3-25 08:14:30
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事物的方面
发表于 2025-3-25 14:58:05
https://doi.org/10.1007/978-3-658-42014-7U-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881, 0.884, 0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in segmenting aneurysms. This work achieved rank one in CADA 2020- Aneurysm Segmentation Challenge.
袋鼠
发表于 2025-3-25 19:43:44
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Flat-Feet
发表于 2025-3-25 21:51:39
Heidi Möller,Thomas Giernalczyksm. The proposed network was trained on the . challenge set of 109 aneurysms. The proposed method achieves an accuracy of 0.64 and an F2-score of 0.73 on the private . challenge test set of 30 aneurysms.
machination
发表于 2025-3-26 02:29:11
Deep Learning-Based 3D U-Net Cerebral Aneurysm Detectiont solutions, with a drastically reduced false-positive rate per patient. The described solution is almost entirely accurate on structures larger than 5 mm in diameter but shows difficulties with smaller aneurysms. We show an F2-score of 0.84 and a false-positive rate of 0.41 on a private test set.
认为
发表于 2025-3-26 06:43:15
3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation ChallengeU-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881, 0.884, 0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in segmenting aneurysms. This work achieved rank one in CADA 2020- Aneurysm Segmentation Challenge.
Rodent
发表于 2025-3-26 10:59:19
CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamicsults of the DNS may serve as inputs for data driven methods to identify qualitative maps of hemodynamic quantities in aneurysms. In this article we report the results of CFD and discuss hypotheses associating the flow characteristics with the rupture risk of aneurysms.
A保存的
发表于 2025-3-26 12:58:18
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轨道
发表于 2025-3-26 20:48:56
0302-9743Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, and took place virtually due to the COVID-19 pandemic. .The 9 regular papers presented in this volume, together with an overview and one in