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Clip in medical imaging: A comprehensive survey
Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …
paradigm, successfully introduces text supervision to vision models. It has shown promising …
[HTML][HTML] Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data
In this study, we address critical barriers hindering the widespread adoption of surgical
navigation in orthopedic surgeries due to limitations such as time constraints, cost …
navigation in orthopedic surgeries due to limitations such as time constraints, cost …
Spineclue: Automatic vertebrae identification using contrastive learning and uncertainty estimation
Vertebrae identification in arbitrary fields-of-view plays a crucial role in diagnosing spine
disease. Most spine CT contain only local regions, such as the neck, chest, and abdomen …
disease. Most spine CT contain only local regions, such as the neck, chest, and abdomen …
Semantics and instance interactive learning for labeling and segmentation of vertebrae in CT images
Automatically labeling and segmenting vertebrae in 3D CT images compose a complex
multi-task problem. Current methods progressively conduct vertebra labeling and semantic …
multi-task problem. Current methods progressively conduct vertebra labeling and semantic …
Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
Automatic parsing of human anatomies at the instance-level from 3D computed tomography
(CT) is a prerequisite step for many clinical applications. The presence of pathologies …
(CT) is a prerequisite step for many clinical applications. The presence of pathologies …
SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation
Automatic and precise multi-class vertebrae segmentation from CT images is crucial for
various clinical applications. However, due to a lack of explicit consistency constraints …
various clinical applications. However, due to a lack of explicit consistency constraints …
VertFound: Synergizing Semantic and Spatial Understanding for Fine-Grained Vertebrae Classification via Foundation Models
Achieving automated vertebrae classification in spine images is a crucial yet challenging
task due to the repetitive nature of adjacent vertebrae and limited fields of view (FoV) …
task due to the repetitive nature of adjacent vertebrae and limited fields of view (FoV) …
Explainable Vertebral Fracture Analysis with Uncertainty Estimation Using Differentiable Rule-Based Classification
We present a novel method for explainable vertebral fracture assessment (XVFA) in low-
dose radiographs using deep neural networks, incorporating vertebra detection and …
dose radiographs using deep neural networks, incorporating vertebra detection and …
Swin-X2S: Reconstructing 3D Shape from 2D Biplanar X-ray with Swin Transformers
K Liu, Z Ying, J **, D Li, P Huang, W Wu… - arxiv preprint arxiv …, 2025 - arxiv.org
The conversion from 2D X-ray to 3D shape holds significant potential for improving
diagnostic efficiency and safety. However, existing reconstruction methods often rely on …
diagnostic efficiency and safety. However, existing reconstruction methods often rely on …
Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification
VW Skärström, L Johansson, J Alvén… - arxiv preprint arxiv …, 2024 - arxiv.org
We present a novel method for explainable vertebral fracture assessment (XVFA) in low-
dose radiographs using deep neural networks, incorporating vertebra detection and …
dose radiographs using deep neural networks, incorporating vertebra detection and …