Assessment of Volumetric Dense Tissue Segmentation in Tomosynthesis Using Deep Virtual Clinical Trials [0.03%]
使用深度虚拟临床试验评估tomosynthesis中的体积致密组织分割
B Barufaldi,J V Gomes,Tm Silva Filho et al.
B Barufaldi et al.
The adoption of artificial intelligence (AI) in medical imaging requires careful evaluation of machine-learning algorithms. We propose the use of a "deep virtual clinical trial" (DeepVCT) method to effectively evaluate the performance of AI...
Xiaoyang Chen,Qin Liu,Hannah H Deng et al.
Xiaoyang Chen et al.
Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships. This oversight potentially leads to seman...
Hao Guan,Pew-Thian Yap,Andrea Bozoki et al.
Hao Guan et al.
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. ...
A cascaded nested network for 3T brain MR image segmentation guided by 7T labeling [0.03%]
级联嵌套网络：利用7T标签指导的3T脑MR图像分割
Jie Wei,Zhengwang Wu,Li Wang et al.
Jie Wei et al.
Accurate segmentation of the brain into gray matter, white matter, and cerebrospinal fluid using magnetic resonance (MR) imaging is critical for visualization and quantification of brain anatomy. Compared to 3T MR images, 7T MR images exhib...
Le Zhang,Ryutaro Tanno,Moucheng Xu et al.
Le Zhang et al.
Supervised machine learning methods have been widely developed for segmentation tasks in recent years. However, the quality of labels has high impact on the predictive performance of these algorithms. This issue is particularly acute in the...
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms [0.03%]
基于关联图的冠状动脉语义标注血管造影图像的图匹配网络
Chen Zhao,Zhihui Xu,Jingfeng Jiang et al.
Chen Zhao et al.
Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in computer-aided coronary artery disease (CAD) diagnosis. Howeve...
Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network [0.03%]
基于形态变换生成对抗网络的脑部磁共振图像纵向预测方法研究
Yunzhi Huang,Sahar Ahmad,Luyi Han et al.
Yunzhi Huang et al.
Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. ...
Semi-automatic muscle segmentation in MR images using deep registration-based label propagation [0.03%]
基于深度注册的标签传播的MR图像半自动肌分割方法
Nathan Decaux,Pierre-Henri Conze,Juliette Ropars et al.
Nathan Decaux et al.
Fully automated approaches based on convolutional neural networks have shown promising performances on muscle segmentation from magnetic resonance (MR) images, but still rely on an extensive amount of training data to achieve valuable resul...
Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation [0.03%]
具有知识蒸馏的动量对比变换器用于COVID-19诊断
Aimei Dong,Jian Liu,Guodong Zhang et al.
Aimei Dong et al.
Intelligent diagnosis has been widely studied in diagnosing novel corona virus disease (COVID-19). Existing deep models typically do not make full use of the global features such as large areas of ground glass opacities, and the local featu...
Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent [0.03%]
具有投影梯度下降的对抗训练中模型梯度偏向方向的量化方法
Ricardo Bigolin Lanfredi,Joyce D Schroeder,Tolga Tasdizen
Ricardo Bigolin Lanfredi
Adversarial training, especially projected gradient descent (PGD), has proven to be a successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect to their inputs hav...