Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm [0.03%]
基于深度学习算法的超低剂量CT与常规剂量CT在肋骨骨折诊断中的准确性比较研究
Peikai Huang,Hongyi Li,Fenghuan Lin et al.
Peikai Huang et al.
The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomography (SD-CT) in discerning recent rib fractures using a deep learning algorithm detection of...
Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification [0.03%]
美国国家癌症研究所2023年医学图像去识别虚拟研讨会综述 第一部分：MIDI工作组报告- 去识别最佳实践与建议、传统方法工具、国际去识别方法及业界 panel 讨论
David Clunie,Fred Prior,Michael Rutherford et al.
David Clunie et al.
De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and...
CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation [0.03%]
基于自动注意力机制的混合模型心血管磁共振图像分割方法研究（CapNet）
Tien Viet Pham,Tu Ngoc Vu,Hoang-Minh-Quang Le et al.
Tien Viet Pham et al.
Deep neural networks have shown excellent performance in medical image segmentation, especially for cardiac images. Transformer-based models, though having advantages over convolutional neural networks due to the ability of long-range depen...
Sunyi Zheng,Xiaonan Cui,Yuxuan Sun et al.
Sunyi Zheng et al.
Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pre-training (CLIP) model has shown remarkable proficiency in understanding natural im...
Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline [0.03%]
国家癌症研究所2023年虚拟医学图像去标识化研讨会综述（第二部分）：病理全片图像的去标识化、去面部处理、人工智能在图像去标识化中的作用以及NCI MIDI数据集和流水线
David Clunie,Adam Taylor,Tom Bisson et al.
David Clunie et al.
De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and...
Minimal Detectable Bone Fracture Gaps in CT Images and Digital Three-Dimensional (3D) Radii Models [0.03%]
CT图像和数字三维模型中最小可检测骨裂缝大小
Martin Bittner-Frank,Andreas Strassl,Ewald Unger et al.
Martin Bittner-Frank et al.
Knowledge of the minimal detectable bone fracture gap is essential in three-dimensional (3D) models, particularly in pre-operative planning of osteosynthesis to avoid overlooking gaps. In this study, defined incisions and bony displacements...
Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization [0.03%]
进化策略能够进行系统且可靠的不确定性量化：关于静息态功能磁共振成像语言半球优势的验证性概念试点研究
Joseph N Stember,Katharine Dishner,Mehrnaz Jenabi et al.
Joseph N Stember et al.
Reliable and trustworthy artificial intelligence (AI), particularly in high-stake medical diagnoses, necessitates effective uncertainty quantification (UQ). Existing UQ methods using model ensembles often introduce invalid variability or co...
Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study [0.03%]
基于深度学习的便携式仰卧胸部X射线中重症监护和急诊医学鼻胃管定位和检测：多中心回顾性研究
Chih-Hung Wang,Tianyu Hwang,Yu-Sen Huang et al.
Chih-Hung Wang et al.
Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs...
Myungeun Lee,Jong Hyo Kim,Wookjin Choi et al.
Myungeun Lee et al.
TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 8...
TransMVAN: Multi-view Aggregation Network with Transformer for Pneumonia Diagnosis [0.03%]
基于Transformer的多视图聚合网络肺炎诊断方法
Xiaohong Wang,Zhongkang Lu,Su Huang et al.
Xiaohong Wang et al.
Automated and accurate classification of pneumonia plays a crucial role in improving the performance of computer-aided diagnosis systems for chest X-ray images. Nevertheless, it is a challenging task due to the difficulty of learning the co...