Titlebook: Clinical Image-Based Procedures; 11th Workshop, CLIP Yufei Chen,Marius George Linguraru,Cristina Oyarzu Conference proceedings 2023 The Ed

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https://doi.org/10.1007/978-981-16-4023-0paper, we propose a feature patch based attention model to improve the classification accuracy of dental caries in CBCT images. We extract overlapping patches from the 3D feature maps and assign every patch with a corresponding weight computed by adaptive learning to achieve automatic screening of r

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,Fast Auto-differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoemented this vectorized version of Siddon’s method in PyTorch, taking advantage of the library’s strong automatic differentiation engine to make this DRR generator fully differentiable with respect to its parameters. Additionally, using GPU-accelerated tensor computation enables our vectorized imple

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,Machine Learning Based Approach for Motion Detection and Estimation in Routinely Acquired Low Resoll is trained to evaluate the severity and time point of possible motion..The model for the first phase achieves a precision of 20.78 % and a recall of 69.57 %, while the model for the second phase reaches a precision of 67.71 % and a recall of 98.49 % to detect non-negligible motion. Despite low pre

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,STAU-Net: A Spatial Structure Attention Network for 3D Coronary Artery Segmentation,ets the loss contextual information by fusing the feature map of the upper decoder. Also, the framework first resamples the input to a fixed size to implement training and up-sample to original size by customized post-processing at output stage. Compared with other related segmentation networks, the

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,Feature Patch Based Attention Model for Dental Caries Classification,paper, we propose a feature patch based attention model to improve the classification accuracy of dental caries in CBCT images. We extract overlapping patches from the 3D feature maps and assign every patch with a corresponding weight computed by adaptive learning to achieve automatic screening of r

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