Deep Ensembles Are Robust to Occasional Catastrophic Failures of Individual DNNs for Organs Segmentations in CT Images [0.03%]
深度集成对CT图像中器官分割中单个DNN偶尔出现的灾难性失败具有鲁棒性
Yury Petrov,Bilal Malik,Jill Fredrickson et al.
Yury Petrov et al.
Deep neural networks (DNNs) have recently showed remarkable performance in various computer vision tasks, including classification and segmentation of medical images. Deep ensembles (an aggregated prediction of multiple DNNs) were shown to ...
Utility of Artificial Intelligence for Real-Time Anatomical Landmark Identification in Ultrasound-Guided Thoracic Paravertebral Block [0.03%]
人工智能在超声引导下胸椎旁阻滞解剖标志实时识别中的应用价值研究
Yaoping Zhao,Shaoqiang Zheng,Nan Cai et al.
Yaoping Zhao et al.
Thoracic paravertebral block (TPVB) is a common method of inducing perioperative analgesia in thoracic and abdominal surgery. Identifying anatomical structures in ultrasound images is very important especially for inexperienced anesthesiolo...
Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults [0.03%]
应用深度学习检测成人创伤超声检查中血腹的存在和位置
Megan M Leo,Ilkay Yildiz Potter,Mohsen Zahiri et al.
Megan M Leo et al.
Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform life...
Visual Image Annotation for Bowel Obstruction: Repeatability and Agreement with Manual Annotation and Neural Networks [0.03%]
可视图像标注在肠梗阻中的重复性和与手动标注及神经网络的吻合度
Paul M Murphy
Paul M Murphy
Bowel obstruction is a common cause of acute abdominal pain. The development of algorithms for automated detection and characterization of bowel obstruction on CT has been limited by the effort required for manual annotation. Visual image a...
Development and Validation of Multi-Omics Thymoma Risk Classification Model Based on Transfer Learning [0.03%]
基于迁移学习的多组学胸腺肿瘤风险分类模型的开发与验证
Wei Liu,Wei Wang,Hanyi Zhang et al.
Wei Liu et al.
The paper aims to develop prediction model that integrates clinical, radiomics, and deep features using transfer learning to stratifying between high and low risk of thymoma. Our study enrolled 150 patients with thymoma (76 low-risk and 74 ...
Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images [0.03%]
基于CT影像的深度卷积神经网络骶髂关节炎（AS相关）自动图像分割与分级诊断
Ke Zhang,Guibo Luo,Wenjuan Li et al.
Ke Zhang et al.
Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading d...
Hongbiao Sun,Wenwen Wang,Fujin He et al.
Hongbiao Sun et al.
Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelli...
Enhancement of Non-Linear Deep Learning Model by Adjusting Confounding Variables for Bone Age Estimation in Pediatric Hand X-rays [0.03%]
通过调整混杂变量改善非线性深度学习模型以改进儿童手部X光片的骨龄估计
Ki Duk Kim,Sunggu Kyung,Miso Jang et al.
Ki Duk Kim et al.
In medicine, confounding variables in a generalized linear model are often adjusted; however, these variables have not yet been exploited in a non-linear deep learning model. Sex plays important role in bone age estimation, and non-linear d...
An Effective Approach to Improve the Automatic Segmentation and Classification Accuracy of Brain Metastasis by Combining Multi-phase Delay Enhanced MR Images [0.03%]
结合多时相增强MR图像有效提高脑转移瘤分割与分类精度的方法研究
Mingming Chen,Yujie Guo,Pengcheng Wang et al.
Mingming Chen et al.
The objective of this study is to analyse the diffusion rule of the contrast media in multi-phase delayed enhanced magnetic resonance (MR) T1 images using radiomics and to construct an automatic classification and segmentation model of brai...
Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI [0.03%]
动态对比增强MRI下的弱监督乳腺病变检测方法
Rong Sun,Chuanling Wei,Zhuoyun Jiang et al.
Rong Sun et al.
Currently, obtaining accurate medical annotations requires high labor and time effort, which largely limits the development of supervised learning-based tumor detection tasks. In this work, we investigated a weakly supervised learning model...