A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI [0.03%]
基于3D影像组学的人工神经网络模型在MRI鉴别椎体良恶性骨折中的应用
Natália S Chiari-Correia,Marcello H Nogueira-Barbosa,Rodolfo Dias Chiari-Correia et al.
Natália S Chiari-Correia et al.
To train an artificial neural network model using 3D radiomic features to differentiate benign from malignant vertebral compression fractures (VCFs) on MRI. This retrospective study analyzed sagittal T1-weighted lumbar spine MRIs from 91 pa...
Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases [0.03%]
用于预测难以解释的正常病例的全球乳房X线摄影影像组学特征
Somphone Siviengphanom,Ziba Gandomkar,Sarah J Lewis et al.
Somphone Siviengphanom et al.
This work aimed to investigate whether global radiomic features (GRFs) from mammograms can predict difficult-to-interpret normal cases (NCs). Assessments from 537 readers interpreting 239 normal mammograms were used to categorise cases as 1...
A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images [0.03%]
基于钼靶X线影像纹理特征的乳腺病变分类的影像组学研究
Nishta Letchumanan,Jeannie Hsiu Ding Wong,Li Kuo Tan et al.
Nishta Letchumanan et al.
This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine ...
A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets [0.03%]
一种鲁棒且可解释的基于结构的算法，用于从超声多数据集中检测器官边界
Tao Peng,Yidong Gu,Ji Zhang et al.
Tao Peng et al.
Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ult...
A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color Fluorescence In Situ Hybridization (FISH) Image and Deep Refined Learning [0.03%]
基于深度精炼学习和四色荧光原位杂交技术的循环遗传异常细胞识别框架
Xu Xu,Congsheng Li,Xingjie Lan et al.
Xu Xu et al.
Circulating genetically abnormal cells (CACs) constitute an important biomarker for cancer diagnosis and prognosis. This biomarker offers high safety, low cost, and high repeatability, which can serve as a key reference in clinical diagnosi...
An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features [0.03%]
结合纹理特征的集成网络模型皮肤异常检测方法
Misaj Sharafudeen,Vinod Chandra S S
Misaj Sharafudeen
Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help medical professionals with early detection. We propose an integrated multi-modal ensemble framewo...
Improving Accuracy of Pneumonia Classification Using Modified DenseNet [0.03%]
使用改进的DenseNet提高肺炎分类精度
Kai Wang,Ping Jiang,Dali Kong et al.
Kai Wang et al.
Ramachandro Majji,Om Prakash P G,R Rajeswari et al.
Ramachandro Majji et al.
IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification meth...
Views on Augmented Reality, Virtual Reality, and 3D Printing in Modern Medicine and Education: A Qualitative Exploration of Expert Opinion [0.03%]
有关增强现实、虚拟现实和3D打印在现代医学和教育中的观点：专家意见的质性研究
Julie Urlings,Guido de Jong,Thomas Maal et al.
Julie Urlings et al.
Although an increased usage and development of 3D technologies is observed in healthcare over the last decades, full integration of these technologies remains challenging. The goal of this project is to qualitatively explore challenges, pea...
Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm [0.03%]
基于深度学习算法利用常规MRI和扩散加权成像鉴别胶质母细胞瘤和单发脑转移瘤
Qingqing Yan,Fuyan Li,Yi Cui et al.
Qingqing Yan et al.
This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI ...