Titlebook: Machine Learning in Medical Imaging; 14th International W Xiaohuan Cao,Xuanang Xu,Xi Ouyang Conference proceedings 2024 The Editor(s) (if a

苦澀

,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

Adornment

,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

GRACE

,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

,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

隨意

,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

流逝

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

,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