Dr. of Applied Computer Science, VR Lab., Beihang University
Multi-view Multi-scale CNNs for Lung Nodule Type Classification from CT Images
<摘要>: Our new paper for lung nodule type classification
<摘要>: MITK和SimpleITK编译使用杂记
Hybrid-feature-guided lung nodule type classification on CT images
<摘要>: Our new paper for CAD/Graphics 2017, Zhangjiajie, China
<摘要>: We have developped a plugin based on MITK to process and visualize the corresponding information in LIDC-IDRI datas.
<摘要>: 刘星龙博士毕业论文
A CADe System for Nodule Detection in Thoracic CT Images based on Artificial Neural Network
<摘要>: Lung cancer has been the leading cause of cancer-related deaths in 2015 in United States. Early detection of lung nodules will undoubtedly increase the five-year survival rate for lung cancer according to prior studies. In this paper, we propose a novel rating method based on geometrical and statistical features to extract initial nodule candidates and an artificial neural network approach to the detection of lung nodules. The novel method is solely based on 3D distribution of neighboring voxels instead of user-specified features. During initial candidates detection, we combine organized region properties calculated from connected component analysis with corresponding voxel value distributions from statistical analysis to reduce false positives while retaining true nodules. Then we devise multiple artificial neural networks (ANNs) trained from massive voxel neighbor sampling of different types of nodules and organize the outputs using a 3D scoring method to identify final nodules. The experiments on 107 CT cases with 252 nodules in LIDC-IDRI data sets have shown that our new method achieves sensitivity of 89.4% while reducing the false positives to 2.0 per case. Our comprehensive experiments have demonstrated our system would be of great assistance for diagnosis of lung nodules in clinical treatments.
<摘要>: Supplementary Material for our Multi-view Multi-scale CNN for Lung Nodule Type Classification from CT Images
<摘要>: 各类经历的项目的图片、视频。
Robust Optimization-based Coronary Artery Labeling from X-Ray Angiograms
<摘要>: In this paper, we present an efficient robust labeling method for coronary arteries from X-ray angiograms based on energy optimization. The fundamental goal of this research is to facilitate the analysis and diagnosis of interventional surgery in the most efficient way, and such effort could also improve the performance during doctor training, and surgery simulation and planning.
<摘要>: In this paper, we present a parallel 4D vessel reconstruction algorithm that simultaneously recovers 3D structure, shape, and motion based on multiple views of X-ray angiograms. The fundamental goal is to assist the analysis and diagnosis of interventional surgery in the most efficient way towards interactive and accurate performance.
<摘要>: 树莓派配置OpenVPN的过程中使用的脚本，以及遇到的问题和解决方法，放在这里当记录。
<摘要>: 记录了Markdown的基本语法。
<摘要>: 记录了Markdown的完整。
<摘要>: 2013年参加CADG13会议相关。
<摘要>: 老板转发的一篇文章。
<摘要>: 快速了解如何使用Papery。
<摘要>: 展示了一些代码和数学公式显示效果。
<摘要>: 基于Papery的第一篇日志。