Incorporating modality-specific intensity prior as text prompt for multimodal myocardial pathology segmentation [0.03%]
融合模式特定强度先验的文本提示在多模态心肌病变分割中的应用
Donggen Fang,Yuliang Gu,Lingyi Yu et al.
Donggen Fang et al.
Accurate myocardial pathology segmentation from multimodal CMR is crucial for myocardial infarction risk assessment and treatment planning. Unlike traditional methods that rely on morphological and geometrical prior for assistance, we propo...
Neural implicit heart coordinates: 3D cardiac shape reconstruction from sparse segmentations [0.03%]
神经隐式心脏坐标：从稀疏分割重建三维心脏形状
Marica Muffoletto,Uxio Hermida,Charlène A Mauger et al.
Marica Muffoletto et al.
Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomica...
Towards generalizable pathology reports via a multimodal LLM with the multicenter in-context learning [0.03%]
基于多模态LLM和多中心In-context Learning的通用病理报告生成方法研究
Yi Li,Zhihao Lin,Qixiang Zhang et al.
Yi Li et al.
Pathology report generation has received increasing attention in recent years. However, existing pathology report generation methods still face two main limitations: (1) these methods utilize image-report datasets where some report contents...
MorphoNet: Morphological sub-region-based structure learning for WSI analysis [0.03%]
基于形态亚区域的结构学习的WSI分析方法 MorphoNet
Fuying Wang,Feng Wu,Ming Hu et al.
Fuying Wang et al.
Representation learning of Whole slide image (WSI) is fundamental to computational pathology, enabling tasks such as tumor subtyping, survival prediction, and cancer grading. Existing methods typically tile WSIs into thousands of small patc...
UniPET: A universal network for high-quality PET image denoising across varied dose reduction factors [0.03%]
一种通用型神经网络用于不同低剂量条件下的高质量图像重建
Zhiwen Yang,Yang Zhou,Haowei Chen et al.
Zhiwen Yang et al.
Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the ...
VCC-DSA: A novel vascular consistency constrained DSA imaging model for motion artifact suppression [0.03%]
一种新的血管一致性约束DSA成像模型用于运动伪影抑制方法(VCC-DSA)
Rongjun Ge,Weilong Mao,Jian Lu et al.
Rongjun Ge et al.
Digital Subtraction Angiography (DSA) is a clinically significant imaging technique for diagnosing cerebrovascular disease, as gold-standard. However, the artifacts caused by motion of high-attenuation tissues such as bones, teeth, and cath...
OphMatcher: Uncertainty-aware self-training on ophthalmic surgical videos for anatomy-constrained matching and intraoprative navigation [0.03%]
基于不确定性的自训练在眼科手术视频解剖约束匹配及术中导航中的应用(OphMatcher)
Puxun Tu,Ce Zheng,Li Luo et al.
Puxun Tu et al.
Surgical navigation plays a crucial role in enhancing precision in ophthalmic surgery, with inter-frame video matching serving as the key enabling technology. However, existing image matchers trained on natural scenes fail to meet the speci...
Point2SSM++: Self-supervised learning of anatomical shape models from point clouds [0.03%]
基于点云的解剖形状模型的自监督学习点到点2ssm++
Jadie Adams,Mokshagna Sai Teja Karanam,Shireen Elhabian
Jadie Adams
Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research. SSM facilitates population-level characterization and quantification of anatomical shapes such as bones an...
Learning with less supervision: A survey of label-efficient learning for medical image analysis [0.03%]
少监督学习在医学图像分析中的应用研究综述
Cheng Jin,Zhengrui Guo,Yi Lin et al.
Cheng Jin et al.
Deep learning has significantly advanced medical imaging analysis (MIA), achieving state-of-the-art performance across diverse clinical tasks. However, its success largely depends on large-scale, high-quality labeled datasets, which are cos...
Foundational model-based geometric consistency monocular depth estimation framework for colonoscopy [0.03%]
结肠镜检查的基于模型的几何一致性单目深度估计框架
Yeqi Liu,Deping Yu,Ling Liu et al.
Yeqi Liu et al.
Colonoscopy remains the gold standard for detecting and treating precancerous polyps, yet it lacks real-time three-dimensional feedback, leading to blind spots and missed lesions. Using 3D reconstruction algorithms can alert clinicians to p...