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Titlebook: Computational Methods and Clinical Applications for Spine Imaging; 6th International Wo Yunliang Cai,Liansheng Wang,Shuo Li Conference proc

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樓主: HEIR
41#
發(fā)表于 2025-3-28 15:54:13 | 只看該作者
42#
發(fā)表于 2025-3-28 20:04:12 | 只看該作者
Springer Undergraduate Mathematics Seriesy predicts the cobb angles from segmentation masks. To alleviate the domain shift problem appeared between training and testing sets, we also conduct a domain adaptation module into network structures. Finally, by ensembling the predictions of different models, our method achieves . SMAPE in the testing set.
43#
發(fā)表于 2025-3-29 00:37:43 | 只看該作者
44#
發(fā)表于 2025-3-29 04:39:35 | 只看該作者
Seg4Reg Networks for Automated Spinal Curvature Estimationy predicts the cobb angles from segmentation masks. To alleviate the domain shift problem appeared between training and testing sets, we also conduct a domain adaptation module into network structures. Finally, by ensembling the predictions of different models, our method achieves . SMAPE in the testing set.
45#
發(fā)表于 2025-3-29 07:46:05 | 只看該作者
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發(fā)表于 2025-3-29 14:28:01 | 只看該作者
Conditioned Variational Auto-encoder for Detecting Osteoporotic Vertebral Fracturesighing regime that maximizes the classification yield. Furthermore, we ‘look into’ the learnt network by investigating the saliency maps, traversing the latent space and demonstrating its smoothness. Finally, we report our results on two datasets, including the publicly available xVertSeg dataset achieving an F1 score of 84%.
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發(fā)表于 2025-3-29 18:44:43 | 只看該作者
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發(fā)表于 2025-3-29 23:44:08 | 只看該作者
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發(fā)表于 2025-3-30 01:22:31 | 只看該作者
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發(fā)表于 2025-3-30 06:26:13 | 只看該作者
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