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Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Wei Chen,Yijie Pan Conference proceedings

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樓主: fundoplication
41#
發(fā)表于 2025-3-28 15:59:03 | 只看該作者
Die Aufgaben der Kostenrechnung,etter focus on and utilize feature information at different scales, and achieves effective skip connections. The proposed model is evaluated on two different medical image segmentation datasets, and the results show that our model has achieved better performance in terms of accuracy.
42#
發(fā)表于 2025-3-28 19:30:43 | 只看該作者
43#
發(fā)表于 2025-3-29 02:56:30 | 只看該作者
https://doi.org/10.1007/978-3-322-84098-1to recover the secret image. The experimental outcomes indicate that the proposed model increases the visual effect of images, with cover images PSNR and SSIM reaching 40.36 dB and 98.18%, respectively. Therefore, the model can effectively hide images during information transmission and prevent atta
44#
發(fā)表于 2025-3-29 05:37:28 | 只看該作者
45#
發(fā)表于 2025-3-29 08:08:01 | 只看該作者
Grundlagen der Lebensmittelmikrobiologie, reconstruction is performed using an inverse wavelet transformation. Experimental results demonstrate that the proposed algorithm not only effectively suppresses complex noise in images and enhances the contrast of clinical pulmonary CT images but also preserves the natural appearance of images an
46#
發(fā)表于 2025-3-29 12:23:47 | 只看該作者
Grundlagen der Lebensmittelmikrobiologien the first stage, we introduce a novel two-decoder architecture with collaborative learning to preliminarily decouple blur features and mitigate the learning complexity of the network. In the second stage, we propose a coupled learning module (CLM) and a feature enhancement block (FEB) to constrain
47#
發(fā)表于 2025-3-29 18:49:35 | 只看該作者
48#
發(fā)表于 2025-3-29 22:17:18 | 只看該作者
49#
發(fā)表于 2025-3-30 01:47:51 | 只看該作者
50#
發(fā)表于 2025-3-30 08:03:26 | 只看該作者
MAPNet: A Multi-scale Attention Pooling Network for Ultrasound Medical Image Segmentationetter focus on and utilize feature information at different scales, and achieves effective skip connections. The proposed model is evaluated on two different medical image segmentation datasets, and the results show that our model has achieved better performance in terms of accuracy.
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