[Application of an interpretable neural network framework based on the LASSO-proj algorithm for warfarin dose prediction] [0.03%]
[LASSO-proj算法的可解释神经网络框架在华法林剂量预测中的应用]
Chenlu Zhong,Ye Zhu,Xiang Gu
Chenlu Zhong
Warfarin, a classic oral anticoagulant, is characterized by a narrow therapeutic window and considerable interindividual variability in dosing requirements. This makes precise dose adjustment challenging in clinical practice and increases t...
[An unsupervised three-dimensional medical image registration method based on shifted window Transformer and convolutional neural network] [0.03%]
一种基于移位窗口Transformer和卷积神经网络的无监督三维医学图像配准方法
Yang Li,Chenmiao Ruan,Dongsheng Ruan
Yang Li
Three-dimensional (3D) deformable image registration plays a critical role in 3D medical image processing. This technique aligns images from different time points, modalities, or individuals in 3D space, enabling the comparison and fusion o...
Yusi Liu,Liangce Qi,Zhaoheng Diao et al.
Yusi Liu et al.
Cross-modal unsupervised domain adaptation (UDA) aims to transfer segmentation models trained on a labeled source modality to an unlabeled target modality. However, existing methods often fail to fully exploit shape priors and intermediate ...
[Detection of neurofibroma combining radiomics and ensemble learning] [0.03%]
基于影像组学和集成学习的神经纤维瘤检测方法研究
Yunpeng Liu,Dangzhi Wencheng,Ying Wang et al.
Yunpeng Liu et al.
This study proposes an automated neurofibroma detection method for whole-body magnetic resonance imaging (WBMRI) based on radiomics and ensemble learning. A dynamic weighted box fusion mechanism integrating two dimensional (2D) object detec...
[Discrimination of macrotrabecular-massive hepatocellular carcinoma based on fusion of multi-phase contrast-enhanced computed tomography radiomics features] [0.03%]
基于多时相增强CT影像组学特征融合的巨块型肝细胞癌鉴别诊断研究
Zhenyang Zhang,Jincheng Xie,Weixiong Zhong et al.
Zhenyang Zhang et al.
The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is a histological variant with higher malignant potential. Non-invasive preoperative identification of MTM-HCC is crucial for precise treatment. Current radiomics-b...
[Image classification of osteoarthritis based on improved shifted windows transformer and graph convolutional networks] [0.03%]
基于改进的移位窗口变压器和图卷积网络的骨关节炎图像分类
Liang Jiang,Hui Cao,Zhiming Ma
Liang Jiang
Osteoarthritis is a common degenerative joint disease, which is often analyzed by X-ray images. However, if there is a lack of clinical experience when reading the films, it is easy to cause misdiagnosis. Although deep learning has made sig...
[Feature distillation multiple instance learning method based on sequence reorganized Mamba] [0.03%]
基于序列重组的Mamba特征提取多示例学习方法
Junying Zeng,Weibin Luo,Jiaxi Zhao et al.
Junying Zeng et al.
Prostate cancer is one of the most prevalent malignancies among men worldwide, and its diagnosis relies heavily on accurate analysis of whole slide imaging (WSI) in histopathology. However, manual interpretation is time-consuming and prone ...
[Adaptive lesion-aware fusion network for joint grading of multiple fundus diseases] [0.03%]
自适应病变感知融合网络联合分级多种眼底疾病
Wei Zeng,Shengwen Guo
Wei Zeng
Diabetic retinopathy (DR) and its complication, diabetic macular edema (DME), are major causes of visual impairment and even blindness. The occurrence of DR and DME is pathologically interconnected, and their clinical diagnoses are closely ...
[A coronary artery plaque segmentation method based on focal weighted accuracy loss function] [0.03%]
[基于焦点加权准确度损失函数的冠状动脉斑块分割方法]
Fei Xiong,Haiming Lu,Linfeng Li et al.
Fei Xiong et al.
Medical images of coronary artery plaque are always accompanied by the situation of extreme class imbalance. The traditional two-step methods locate the region of interest (ROI) in the sample firstly, and then segment the sample within the ...
[Automatic detection and visualization of myocardial infarction in electrocardiograms based on an interpretable deep learning model] [0.03%]
基于可解释的深度学习模型的心电图自动化检测和可视化心肌梗死技术
Yue Zhang,Yifei Zhang,Baojie Xie et al.
Yue Zhang et al.
Automated detection of myocardial infarction (MI) is crucial for preventing sudden cardiac death and enabling early intervention in cardiovascular diseases. This paper proposes a deep learning framework based on a lightweight convolutional ...