MPLDM: Multi-modal prosthetic loosening diagnostic model for total hip arthroplasty [0.03%]
用于全髋关节置换的多模态人工关节假体松动诊断模型(MPLDM)
Xiao Chen,Pang Lyu,Wencheng Han et al.
Xiao Chen et al.
Total hip arthroplasty (THA) is an effective procedure for restoring hip joint function and typically yields satisfactory clinical outcomes. Aseptic loosening and periprosthetic joint infection (PJI) are severe complications following total...
GloW-VSNet: A scribble-based weakly supervised framework for global-view vitiligo lesion segmentation [0.03%]
基于少量标注的全局视角白癜风病变区域分割模型GloW-VSNet
Yuheng Wang,Yuhan Zheng,Chloe Yue et al.
Yuheng Wang et al.
Vitiligo lesion identification is essential for quantifying disease severity, monitoring disease progression and assessing treatment response, particularly for objective quantification. However, segmenting vitiligo lesions from clinical ima...
Non-contrast CT esophageal varices grading through clinical prior-enhanced multi-organ analysis [0.03%]
基于临床增强信息的CT食管静脉曲张分级方法研究
Xiaoming Zhang,Chunli Li,Jiacheng Hao et al.
Xiaoming Zhang et al.
Esophageal varices (EV) represent a critical complication of portal hypertension, affecting approximately 60% of cirrhosis patients with a significant bleeding risk of ∼ 30%. While traditionally diagnosed through invasive endoscopy, non-co...
Interpretable classification of endomicroscopic brain data via saliency consistent contrastive learning [0.03%]
基于显著性一致对比学习的内窥脑数据解释性分类
Chi Xu,Alfie Roddan,Irini Kakaletri et al.
Chi Xu et al.
In neurosurgery, accurate brain tissue characterization via probe-based Confocal Laser Endomicroscopy (pCLE) has become popular for guiding surgical decisions and ensuring safe tumour resections. In order to enable surgeons to trust a tissu...
CHAP: Channel-spatial hierarchical adversarial perturbation for semi-supervised medical image segmentation [0.03%]
基于通道-空间层次对抗扰动的半监督医学图像分割方法
Si-Ping Zhou,Zhi-Fang Gong,Kai-Ni Wang et al.
Si-Ping Zhou et al.
Semi-supervised medical image segmentation (SSMIS) methods predominantly rely on consistency regularization to reinforce invariant feature learning under perturbations. However, the reliance on uniform perturbation strategies makes SSMIS mo...
Template-guided reconstruction of pulmonary segments with neural implicit functions [0.03%]
基于神经隐式函数的模板引导肺段重建技术
Kangxian Xie,Yufei Zhu,Kaiming Kuang et al.
Kangxian Xie et al.
High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement of the target reconstruction, conventional deep learning-ba...
Complex wavelet-based Transformer for neurodevelopmental disorder diagnosis via direct modeling of real and imaginary components [0.03%]
基于复小波的Transformer直接建模实部和虚部以进行神经发育障碍诊断的方法
Ah-Yeong Jeong,Da-Woon Heo,Heung-Il Suk
Ah-Yeong Jeong
Resting-state functional magnetic resonance imaging (rs-fMRI) measures intrinsic neural activity, and analyzing its frequency-domain characteristics provides insights into brain dynamics. Owing to these properties, rs-fMRI is widely used to...
Extreme cardiac MRI analysis under respiratory motion: Results of the CMRxMotion challenge [0.03%]
极端心脏MRI分析在呼吸运动下进行：CMRxMotion挑战的结果
Kang Wang,Chen Qin,Zhang Shi et al.
Kang Wang et al.
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on the availability of high-quality, artifact-free images. In ...
CLIP-Guided Generative network for pathology nuclei image augmentation [0.03%]
基于CLIP的病理学核图像增强生成模型网络
Yanan Zhang,Qingyang Liu,Qian Chen et al.
Yanan Zhang et al.
Nuclei segmentation and classification play a crucial role in the quantitative analysis of computational pathology (CPath). However, the challenge of creating a large volume of labeled pathology nuclei images due to annotation costs has sig...
Unsupervised anomaly detection in medical imaging using aggregated normative diffusion [0.03%]
基于聚合规范扩散的医学影像无监督异常检测
Alexander Frotscher,Jaivardhan Kapoor,Thomas Wolfers et al.
Alexander Frotscher et al.
Early detection of anomalies in medical images such as brain magnetic resonance imaging (MRI) is highly relevant for diagnosis and treatment of many medical conditions. Supervised machine learning methods are limited to a small number of pa...