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Titlebook: Cerebral Aneurysm Detection and Analysis; First Challenge, CAD Anja Hennemuth,Leonid Goubergrits,Jan-Martin Kuhni Conference proceedings 20

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樓主: Lampoon
31#
發(fā)表于 2025-3-26 21:47:10 | 只看該作者
https://doi.org/10.1007/978-3-658-42014-7 that we configure the 3D U-Net with a large patch size, which can obtain the large context. Our method ranked second on the MICCAI 2020 CADA testing dataset with an average Jaccard of 0.7593. Our code and trained models are publicly available at ..
32#
發(fā)表于 2025-3-27 02:10:30 | 只看該作者
Organisationale Machtbeziehungen im Wandelosed. We applied a variety of methods to extract features of cerebral aneurysm images and 3D modeling, and used XGBoost and fully connected neural network for classification and analysis respectively. The method achieved an F2-score of 0.862 on the test set of CADA 2020.
33#
發(fā)表于 2025-3-27 08:19:38 | 只看該作者
34#
發(fā)表于 2025-3-27 11:08:58 | 只看該作者
Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Networkosed. We applied a variety of methods to extract features of cerebral aneurysm images and 3D modeling, and used XGBoost and fully connected neural network for classification and analysis respectively. The method achieved an F2-score of 0.862 on the test set of CADA 2020.
35#
發(fā)表于 2025-3-27 17:17:24 | 只看該作者
Heidi M?ller,Thomas Giernalczykaccuracy across various models fed with the aneurysm site encoding. A K-nearest neighbors method shows the best results during our model selection with an F2-score of 0.7 and an accuracy of 0.73 on the relatively small private test set with 22 individuals and 30 aneurysms.
36#
發(fā)表于 2025-3-27 19:38:41 | 只看該作者
37#
發(fā)表于 2025-3-27 23:34:47 | 只看該作者
38#
發(fā)表于 2025-3-28 04:24:19 | 只看該作者
https://doi.org/10.1007/978-3-030-72862-53D imaging; artificial intelligence; computer graphics; computer systems; computer vision; deep learning;
39#
發(fā)表于 2025-3-28 06:34:28 | 只看該作者
40#
發(fā)表于 2025-3-28 12:12:18 | 只看該作者
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