Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data [0.03%]
基于多中心腹部MRI数据的ComBat调和对放射组学和深度特征的影响研究
Wei Jia,Hailong Li,Redha Ali et al.
Wei Jia et al.
ComBat harmonization has been developed to remove non-biological variations for data in multi-center research applying artificial intelligence (AI). We investigated the effectiveness of ComBat harmonization on radiomic and deep features ext...
Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine [0.03%]
基于腰椎磁共振矢状位图像的图形神经网络椎间盘三维影像自动化及Pfirrmann分级
David Baur,Richard Bieck,Johann Berger et al.
David Baur et al.
This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classificatio...
Deep Learning for Automated Classification of Hip Hardware on Radiographs [0.03%]
基于X射线片的髋关节内固定物自动分类深度学习方法
Yuntong Ma,Justin L Bauer,Acacia H Yoon et al.
Yuntong Ma et al.
Purpose: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency amo...
Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance) [0.03%]
卷积神经网络在胸膜间皮瘤分割中的应用：概率图阈值分析（CALGB 30901，联盟）
Mena Shenouda,Eyjólfur Gudmundsson,Feng Li et al.
Mena Shenouda et al.
The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were...
Screening Patient Misidentification Errors Using a Deep Learning Model of Chest Radiography: A Seven Reader Study [0.03%]
基于胸部X光片的深度学习模型筛查患者误识别错误：一项针对七名读者的研究
Kiduk Kim,Kyungjin Cho,Yujeong Eo et al.
Kiduk Kim et al.
We aimed to evaluate the ability of deep learning (DL) models to identify patients from a paired chest radiograph (CXR) and compare their performance with that of human experts. In this retrospective study, patient identification DL models ...
A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention [0.03%]
一种基于双分支结构和多尺度残差注意力的低剂量CT去噪网络
Ju Zhang,Lieli Ye,Weiwei Gong et al.
Ju Zhang et al.
Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel tw...
Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis [0.03%]
基于机器学习的帕金森病诊断的性别特异性影像生物标志物研究
Yifeng Yang,Liangyun Hu,Yang Chen et al.
Yifeng Yang et al.
This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and vari...
PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration [0.03%]
PelviNet：一种协作多智能体卷积网络用于增强盆腔图像配准
Rguibi Zakaria,Hajami Abdelmajid,Zitouni Dya et al.
Rguibi Zakaria et al.
PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agen...
Feasibility of Three-Dimension Chemical Exchange Saturation Transfer MRI for Predicting Tumor and Node Staging in Rectal Adenocarcinoma: An Exploration of Optimal ROI Measurement [0.03%]
三维化学交换饱和转移磁共振成像预测直肠腺癌肿瘤及淋巴结分期：最佳感兴趣区测量的探索性研究
Xiao Wang,Wenguang Liu,Ismail Bilal Masokano et al.
Xiao Wang et al.
To investigate the feasibility of predicting rectal adenocarcinoma (RA) tumor (T) and node (N) staging from an optimal ROI measurement using amide proton transfer weighted-signal intensity (APTw-SI) and magnetization transfer (MT) derived f...
Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features [0.03%]
基于离散小波的中心对称局部二值模式及统计特征的糖尿病视网膜病变检测方法研究
Imtiyaz Ahmad,Vibhav Prakash Singh,Manoj Madhava Gore
Imtiyaz Ahmad
Computer-aided diagnosis (CAD) system assists ophthalmologists in early diabetic retinopathy (DR) detection by automating the analysis of retinal images, enabling timely intervention and treatment. This paper introduces a novel CAD system b...