Application of thin-slice and accelerated T1-weighted GRE sequences in 1.5T abdominal magnetic resonance imaging using deep learning image reconstruction [0.03%]
基于深度学习图像重建的1.5T腹部磁共振成像快速T₁加权GRE序列的应用研究
Natalie S Joos,Saif Afat,Marcel Dominik Nickel et al.
Natalie S Joos et al.
Purpose: Deep-learning (DL)-based image reconstruction (DLR) is a key technique for reducing acquisition time (TA) and increasing morphologic resolution in abdominal magnetic resonance imaging (MRI). We aim to compare the...
Patch relevance estimation and multilabel augmentation for weakly supervised histopathology image classification [0.03%]
弱监督下组织病理图像分类的补丁相关性评估与多标签数据增强方法研究
Bulut Aygunes,Ramazan Gokberk Cinbis,Selim Aksoy
Bulut Aygunes
Purpose: Weakly supervised learning (WSL) is widely used for histopathological image analysis by modeling images as sets of fixed-size patches and utilizing image-level diagnoses as weak labels. However, in multiclass cla...
Benchmarking of deep learning methods for generic MRI multi-organ abdominal segmentation [0.03%]
深度学习在MRI多器官腹部分割中的基准研究
Deepa Krishnaswamy,Cosmin Ciausu,Steve Pieper et al.
Deepa Krishnaswamy et al.
Purpose: Recent advances in deep learning have led to robust automated tools for segmentation of abdominal computed tomography (CT). Meanwhile, segmentation of magnetic resonance imaging (MRI) is substantially more challe...
Multiscale attention network with structure guidance for colorectal polyp segmentation [0.03%]
一种基于结构导向的多尺度注意力网络用于结肠息肉分割
Yang Yang,Jie Gao,Lanling Zeng et al.
Yang Yang et al.
Purpose: Accurate segmentation and precise delineation of colorectal polyp structures are crucial for early clinical diagnosis and treatment planning. However, existing polyp segmentation techniques face significant chall...
Sonal Shukla,Scott Doyle
Sonal Shukla
Purpose: Artificial intelligence has emerged as a powerful technique for data analysis and predictive modeling. However, traditional centralized learning methods, which require aggregating large and diverse datasets at a ...
Cross-modality 3D MRI synthesis via cycle-guided denoising diffusion probability model [0.03%]
基于循环去噪扩散概率模型的跨模态三维磁共振图像合成方法
Mingzhe Hu,Shaoyan Pan,Chih-Wei Chang et al.
Mingzhe Hu et al.
Purpose: We propose a deep learning framework, the cycle-guided denoising diffusion probability model (CG-DDPM), for cross-modality magnetic resonance imaging (MRI) synthesis. The CG-DDPM aims to generate high-quality MRI...
Scribble-supervised method for cardiac tissue segmentation using position and temporal contrastive information [0.03%]
基于位置和时间对比信息的心肌组织 scribble监督分割方法
Xiaoxuan Ma,Yingao Du,Kuncheng Lian
Xiaoxuan Ma
Purpose: Accurate pixel-level segmentation is essential for medical image analysis, particularly in assisting diagnosis and treatment planning. However, fully supervised learning methods rely heavily on high-quality annot...
Automated coronary calcium detection and scoring on multicenter, multiprotocol noncontrast CT [0.03%]
多中心、多协议非对比度CT的冠状钙化自动检测和评分
Andrew M Nguyen,Jianfei Liu,Tejas Sudharshan Mathai et al.
Andrew M Nguyen et al.
Purpose: Coronary artery disease is the leading global cause of mortality. Automated detection and scoring of calcified plaques can help cardiovascular risk assessment. We propose a deep learning method for automatic dete...
Mary N Henderson,David B Jordan,Zong-Ming Li
Mary N Henderson
Purpose: The purpose of this study was to assess the variations in shear-wave speed (SWS) in individual thenar muscles under varied pinch forces in healthy adults. It was hypothesized that (1) SWS would vary among the ind...
Interpretable convolutional neural network for autism diagnosis support in children using structural magnetic resonance imaging datasets [0.03%]
基于结构磁共振图像数据的儿童自闭症诊断辅助的可解释卷积神经网络
Garazi Casillas Martinez,Anthony Winder,Emma A M Stanley et al.
Garazi Casillas Martinez et al.
Purpose: Autism is one of the most common neurodevelopmental conditions, and it is characterized by restricted, repetitive behaviors and social difficulties that affect daily functioning. It is challenging to provide an e...