Titlebook: Machine Learning in Medical Imaging; 13th International W Chunfeng Lian,Xiaohuan Cao,Zhiming Cui Conference proceedings 2022 Springer Natur

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,Function MRI Representation Learning via?Self-supervised Transformer for?Automated Brain Disorder Aal information of FCNs for MDD diagnosis. Experimental results on 282 MDD patients and 251 healthy control (HC) subjects demonstrate that our method outperforms several competing methods in MDD identification based on rs-fMRI data. Besides, based on our learned fully connected graphs, we can detect

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,Predicting Age-related Macular Degeneration Progression with?Longitudinal Fundus Images Using Deep baselines on the Age-Related Eye Disease Study, one of the largest longitudinal AMD cohorts with CFPs. The proposed models outperformed the baseline models that utilized only single-visit CFPs to predict the risk of late AMD (0.879 vs 0.868 in AUC for 2-year prediction, and 0.879 vs 0.862 for 5-year

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,Region-Guided Channel-Wise Attention Network for?Accelerated MRI Reconstruction,l networks. To address this issue, we propose a novel channel-wise attention which is implemented under the guidance of implicitly learned spatial semantics. We incorporate the proposed attention module in a deep network cascade for fast MRI reconstruction. In experiments, we demonstrate that the pr

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,Rethinking Degradation: Radiograph Super-Resolution via?AID-SRGAN, its counterparts. ., our proposed method achieves 31.90 of PSNR with a scale factor of ., which is 7.05% higher than that obtained by recent work, SPSR ?[.]. Our dataset and code will be made available at: ..

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,3D Segmentation with?Fully Trainable Gabor Kernels and?Pearson’s Correlation Coefficient,ccurate, robust to learning rates, and does not require manual weight selections. Experiments on 43 3D brain magnetic resonance images with 19 anatomical structures show that, using the proposed loss function with a proper combination of conventional and Gabor-based kernels, we can train a network w

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,A More Design-Flexible Medical Transformer for?Volumetric Image Segmentation,onal information of each 3D patch. Positional bias can also enrich attention diversities. Moreover, we give detailed reasons why we choose the convolution-based decoder instead of recently proposed Swin Transformer blocks after preliminary experiments on the decoder design. Finally, we propose the C

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,Plug-and-Play Shape Refinement Framework for?Multi-site and?Lifespan Brain Skull Stripping, multi-site and lifespan skull stripping. To deal with the domain shift between multi-site lifespan datasets, we take advantage of the brain shape prior, which is invariant to imaging parameters and ages. Experiments demonstrate that our framework can outperform the state-of-the-art methods on multi

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