Flat-Feet
发表于 2025-3-30 09:15:14
BHSD: A 3D Multi-class Brain Hemorrhage Segmentation Dataset,ity of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. The dataset and checkpoint is available at ..
蘑菇
发表于 2025-3-30 12:24:31
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HERE
发表于 2025-3-30 18:56:30
,Cross-Domain Iterative Network for Simultaneous Denoising, Limited-Angle Reconstruction, and AttenuNet, paired projection- and image-domain networks are end-to-end connected to fuse the cross-domain emission and anatomical information in multiple iterations. Adaptive Weight Recalibrators (AWR) adjust the multi-channel input features to further enhance prediction accuracy. Our experiments using cl
amplitude
发表于 2025-3-30 23:32:24
,Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion,erarchically extract conditional features and conduct element-wise modulation. Our experimental results on the publicly available HCP-1200 dataset demonstrate the high-fidelity super-resolution capability of HiFi-Diff and its efficacy in enhancing downstream segmentation performance.
全面
发表于 2025-3-31 03:25:49
,Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learni for pre-training the network in a supervised manner. In experiments, we show that such a network can be trained to reconstruct 3D images using simulated down-sampled adult images with much better image quality and image segmentation accuracy. Then, we illustrate that the proposed C-SIR approach gen
incite
发表于 2025-3-31 07:18:47
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Cpap155
发表于 2025-3-31 10:13:24
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tic-douloureux
发表于 2025-3-31 14:28:06
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陪审团
发表于 2025-3-31 18:30:04
,Accelerated MRI Reconstruction via Dynamic Deformable Alignment Based Transformer,ice features using dynamic deformable convolution and extract local non-local features before merging information. We adapt input variations by aggregating deformable convolution kernel weights and biases through a dynamic weight predictor. Extensive experiments on Stanford2D, Stanford3D, and large-
Hallowed
发表于 2025-3-31 22:30:20
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