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Titlebook: Machine Learning for Medical Image Reconstruction; 4th International Wo Nandinee Haq,Patricia Johnson,Jaejun Yoo Conference proceedings 202

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樓主: chondrocyte
11#
發(fā)表于 2025-3-23 13:03:52 | 只看該作者
Efficient Image Registration Network for?Non-Rigid Cardiac Motion Estimationrdiac CINE MRIs indicate that the proposed method outperforms the competing approaches substantially, with more than 25% reduction in residual photometric error, and up?to 100. faster inference speed compared to conventional methods.
12#
發(fā)表于 2025-3-23 16:23:33 | 只看該作者
13#
發(fā)表于 2025-3-23 18:04:31 | 只看該作者
14#
發(fā)表于 2025-3-24 01:49:19 | 只看該作者
Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in?Fluoroscopic Imagingoom monitor. On the other hand, we present a video denoising method which executes orders of magnitude faster while achieving state-of-the-art performance. This provides compelling potential for real-time clinical application in fluoroscopic imaging.
15#
發(fā)表于 2025-3-24 02:57:03 | 只看該作者
16#
發(fā)表于 2025-3-24 07:01:23 | 只看該作者
Deep MRI Reconstruction with Generative Vision Transformerstention vision transformers. Cross-attention mechanism between latents and image features serve to enhance representational learning of local and global context. Meanwhile, latent and noise injections at each network layer permit fine control of generated image features, improving model invertibilit
17#
發(fā)表于 2025-3-24 11:34:19 | 只看該作者
Distortion Removal and Deblurring of Single-Shot DWI MRI Scanse of deblurred EPI-DWI images for performing accurate medical diagnosis and multi parametric longitudinal analysis in brain tumors. We use data augmentation, dilated convolution, and ELU (exponential linear unit) to design a suitable architecture that achieves superior performance in terms of accura
18#
發(fā)表于 2025-3-24 18:12:51 | 只看該作者
19#
發(fā)表于 2025-3-24 21:26:02 | 只看該作者
20#
發(fā)表于 2025-3-25 01:39:23 | 只看該作者
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolutionain loss as a constraint to further improve the quality of the RefSR results with fine details and without obvious artifacts. To the best of our knowledge, this is the first paper utilizing the frequency domain for the loss functions in the field of super-resolution. We achieved good results in eval
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