苦涩
发表于 2025-3-23 11:43:22
,unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features,features. The method is versatile for diverse modalities and tasks, as it does not require domain knowledge, paired data samples, or labels. During test time unORANIC is applied to potentially corrupted images, orthogonalizing their anatomy and characteristic components, to subsequently reconstruct
inclusive
发表于 2025-3-23 15:22:23
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Adornment
发表于 2025-3-23 21:07:17
,Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos Using NeRFs,To overcome the visual constraints associated with laparoscopy, the use of laparoscopic images and videos to reconstruct the three-dimensional (3-D) anatomical structure of the abdomen has proven to be a promising approach. Neural Radiance Fields (NeRFs) have recently gained attention thanks to thei
GRIPE
发表于 2025-3-24 01:55:28
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GRACE
发表于 2025-3-24 05:30:30
,Accelerated MRI Reconstruction via Dynamic Deformable Alignment Based Transformer,pressed sensing methods were developed. Parallel imaging acquires multiple anatomy views simultaneously, while compressed sensing acquires fewer samples than traditional methods. However, reconstructing images from undersampled multi-coil data remains challenging. Existing methods concatenate input
Endearing
发表于 2025-3-24 10:10:38
,Deformable Cross-Attention Transformer for Medical Image Registration, registration. Recent advancements in designing registration Transformers have focused on using cross-attention (CA) to enable a more precise understanding of spatial correspondences between moving and fixed images. Here, we propose a novel CA mechanism that computes windowed attention using deforma
随意
发表于 2025-3-24 14:36:57
,Deformable Medical Image Registration Under Distribution Shifts with Neural Instance Optimization,iations in anatomy and contrast changes with different imaging protocols. Gradient descent-based instance optimization is often introduced to refine the solution of deep-learning methods, but the performance gain is minimal due to the high degree of freedom in the solution and the absence of robust
Sigmoidoscopy
发表于 2025-3-24 15:14:24
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流逝
发表于 2025-3-24 22:49:25
BHSD: A 3D Multi-class Brain Hemorrhage Segmentation Dataset,ors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-cl
CYN
发表于 2025-3-25 02:20:57
,Contrastive Learning-Based Breast Tumor Segmentation in DCE-MRI,tative morphological and functional information, thereby assisting subsequent diagnosis and treatment. However, many existing methods mainly focus on features within tumor regions and neglect enhanced background tissues, leading to the potential over-segmentation problem. To better distinguish tumor